ALBERT

Description: This study proposes a quantitative measurement of split of the second heart sound (S2) based on nonstationary signal decomposition to deal with overlaps and energy modeling of the subcomponents of S2. The second heart sound includes aortic (A2) and pulmonic (P2) closure sounds. However, the split detection is obscured due to A2-P2 overlap and low energy of P2. To identify such split, HVD method is used to decompose the S2 into a number of components while preserving the phase information. Further, A2s and P2s are localized using smoothed pseudo Wigner-Ville distribution followed by reassignment method. Finally, the split iscalculated by taking the differences between the means of time indices of A2s and P2s. Experiments on total 33 clips of S2 signals are performed for evaluation of the method. The mean ± standard deviation of the split is 34.7 ± 4.6 ms. The method measures the splitefficiently, even when A2-P2 overlap is ≤ 20 ms and the normalized peak temporal ratio of P2 to A2 is low (≥ 0.22). This proposed method thus, demonstrates its robustness by defining split detectability (SDT), the split detection aptness through detecting P2s, by measuring upto 96 percent. Such findings reveal the effectiveness of the method as competent against the other baselines, especially for A2-P2 overlaps and low energy P2.

Description: Post-acquisition denoising of magnetic resonance (MR) images is an important step to improve any quantitative measurement of the acquired data. In this paper, assuming a Rician noise model, a new filtering method based on the linear minimum mean square error (LMMSE) estimation is introduced, which employs the self-similarity property of the MR data to restore the noise-less signal. This method takes into account the structural characteristics of images and the Bayesian mean square error (Bmse) of the estimator to address the denoising problem. In general, a twofold data processing approach is developed; first, the noisy MR data is processed using a patch-based L 2 -norm similarity measure to provide the primary set of samples required for the estimation process. Afterwards, the Bmse of the estimator is derived as the optimization function to analyze the pre-selected samples and minimize the error between the estimated and the underlying signal. Compared to the LMMSE method and also its recently proposed SNR-adapted realization (SNLMMSE), the optimized way of choosing the samples together with the automatic adjustment of the filtering parameters lead to a more robust estimation performance with our approach. Experimental results show the competitive performance of the proposed method in comparison with related state-of-the-art methods.

Description: Large-scale ad hoc analytics of genomic data is popular using the R-programming language supported by over 700 software packages provided by Bioconductor. More recently, analytical jobs are benefitting from on-demand computing and storage, their scalability and their low maintenance cost, all of which are offered by the cloud. While biologists and bioinformaticists can take an analytical job and execute it on their personal workstations, it remains challenging to seamlessly execute the job on the cloud infrastructure without extensive knowledge of the cloud dashboard. How analytical jobs can not only with minimum effort be executed on the cloud, but also how both the resources and data required by the job can be managed is explored in this paper. An open-source light-weight framework for executing R-scripts using Bioconductor packages, referred to as ‘RBioCloud’, is designed and developed. RBioCloud offers a set of simple command-line tools for managing the cloud resources, the data and the execution of the job. Three biological test cases validate the feasibility of RBioCloud. The framework is available from http://www.rbiocloud.com .

Description: Of major interest to translational genomics is the intervention in gene regulatory networks (GRNs) to affect cell behavior; in particular, to alter pathological phenotypes. Owing to the complexity of GRNs, accurate network inference is practically challenging and GRN models often contain considerable amounts of uncertainty. Considering the cost and time required for conducting biological experiments, it is desirable to have a systematic method for prioritizing potential experiments so that an experiment can be chosen to optimally reduce network uncertainty. Moreover, from a translational perspective it is crucial that GRN uncertainty be quantified and reduced in a manner that pertains to the operational cost that it induces, such as the cost of network intervention. In this work, we utilize the concept of mean objective cost of uncertainty (MOCU) to propose a novel framework for optimal experimental design. In the proposed framework, potential experiments are prioritized based on the MOCU expected to remain after conducting the experiment. Based on this prioritization, one can select an optimal experiment with the largest potential to reduce the pertinent uncertainty present in the current network model. We demonstrate the effectiveness of the proposed method via extensive simulations based on synthetic and real gene regulatory networks.

Description: A novel approach to Contact Map Overlap (CMO) problem is proposed using the two dimensional clusters present in the contact maps. Each protein is represented as a set of the non-trivial clusters of contacts extracted from its contact map. The approach involves finding matching regions between the two contact maps using approximate 2D-pattern matching algorithm and dynamic programming technique. These matched pairs of small contact maps are submitted in parallel to a fast heuristic CMO algorithm. The approach facilitates parallelization at this level since all the pairs of contact maps can be submitted to the algorithm in parallel. Then, a merge algorithm is used in order to obtain the overall alignment. As a proof of concept, MSVNS, a heuristic CMO algorithm is used for global as well as local alignment. The divide and conquer approach is evaluated for two benchmark data sets that of Skolnick and Ding et al. It is interesting to note that along with achieving saving of time, better overlap is also obtained for certain protein folds.

Description: Canalizing genes possess broad regulatory power over a wide swath of regulatory processes. On the other hand, it has been hypothesized that the phenomenon of intrinsically multivariate prediction (IMP) is associated with canalization. However, applications have relied on user-selectable thresholds on the IMP score to decide on the presence of IMP. A methodology is developed here that avoids arbitrary thresholds, by providing a statistical test for the IMP score. In addition, the proposed procedure allows the incorporation of prior knowledge if available, which can alleviate the problem of loss of power due to small sample sizes. The issue of multiplicity of tests is addressed by family-wise error rate (FWER) and false discovery rate (FDR) controlling approaches. The proposed methodology is demonstrated by experiments using synthetic and real gene-expression data from studies on melanoma and ionizing radiation (IR) responsive genes. The results with the real data identified DUSP1 and p53, two well-known canalizing genes associated with melanoma and IR response, respectively, as the genes with a clear majority of IMP predictor pairs. This validates the potential of the proposed methodology as a tool for discovery of canalizing genes from binary gene-expression data. The procedure is made available through an R package.

Description: Programming languages like C and Ada combined with proprietary embedded operating systems have dominated the real-time application space for decades. The new C++11 standard includes native, language-level support for concurrency, a required feature for any nontrivial event-oriented real-time software. Threads, Locks, and Atomics now exist to provide the necessary tools to build the structures that make up the foundation of a complex real-time system. The National Spherical Torus Experiment Upgrade (NSTX-U) at the Princeton Plasma Physics Laboratory (PPPL) is breaking new ground with the language as applied to the needs of fusion devices. A new Digital Coil Protection System (DCPS) will serve as the main protection mechanism for the magnetic coils, and it is written entirely in C++11 running on Concurrent Computer Corporation’s real-time operating system, RedHawk Linux. It runs over 600 algorithms in a 5 kHz control loop that determine whether or not to shut down operations before physical damage occurs. To accomplish this, NSTX-U engineers developed software tools that do not currently exist elsewhere, including real-time atomic synchronization, real-time containers, and a real-time logging framework. Together with a recent (and carefully configured) version of the GCC compiler, these tools enable data acquisition, processing, and output using a conventional operating system to meet a hard real-time deadline (that is, missing one periodic is a failure) of 200 microseconds.

Description: Thallium-bromide (TlBr) is currently under investigation as an alternative room-temperature semiconductor gamma-ray spectrometer due to its favorable material properties (large bandgap, high atomic numbers, and high density). Previous work has shown that 5 mm thick pixelated TlBr detectors can achieve sub-1% FWHM energy resolution at 662 keV for single-pixel events. These results are limited to $ - 20{^ circ }{rm C}$ operation where detector performance is stable. During the first one to five days of applied bias at $ - 20{^ circ }{rm C}$ , many TlBr detectors undergo a conditioning phase, where the energy resolution improves and the depth-dependent electron drift velocity stabilizes. In this work, the spectroscopic performance, drift velocity, and freed electron concentrations of multiple 5 mm thick pixelated TlBr detectors are monitored throughout the conditioning phase. Additionally, conditioning is performed twice on the same detector at different times to show that improvement mechanisms relax when the detector is stored without bias. We conclude that the improved spectroscopy results from internal electric field stabilization and uniformity caused by fewer trapped electrons.

Description: Silicon Photomultipliers (SiPMs) are attractive candidates for light detectors for next generation liquid xenon double-beta decay experiments, like nEXO (next Enriched Xenon Observatory). In this paper we discuss the requirements that the SiPMs must satisfy in order to be suitable for nEXO and similar experiments, describe the two test setups operated by the nEXO collaboration, and present the results of characterization of SiPMs from several vendors. In particular, we find that the photon detection efficiency at the peak of xenon scintillation light emission (175-178 nm) approaches the nEXO requirements for tested FBK and Hamamatsu devices. Additionally, the nEXO collaboration performed radio-assay of several grams of bare FBK devices using neutron activation analysis, indicating levels of $^{40}{rm K}$ , $^{232}{rm Th}$ , and $^{238}{rm U}$ of the order of $ 〈 0.15$ , ( $ 6.9cdot 10^{ - 4} - 1.3 cdot 10^{ - 2}$ ), and $ 〈 0.11 ~hbox{mBq}/hbox{kg}$ , respectively.

Description: It is very important for plant operators to be informed of the departure from nucleate boiling ratio (DNBR) to prevent the fuel cladding from melting and a boiling crisis in a nuclear reactor. The reactor core monitoring and protection systems require a minimum DNBR value to monitor reactor coolant conditions. In this study, in order to estimate the minimum DNBR value, a cascaded fuzzy neural network (CFNN) method was used. The CFNN model can be used to estimate the minimum DNBR value through the process of adding fuzzy neural networks (FNNs) repeatedly. The proposed DNBR estimation algorithm was verified by applying the nuclear and thermal data acquired from many numerical simulations of the optimized power reactor 1000 (OPR1000). The CFNN model was compared to previously developed models and was found to be superior to them. Therefore, this model can be used to effectively monitor and predict the minimum DNBR in the reactor core.

Description: The Regression Network plugin for Cytoscape ( RegNetC ) implements the RegNet algorithm for the inference of transcriptional association network from gene expression profiles. This algorithm is a model tree-based method to detect the relationship between each gene and the remaining genes simultaneously instead of analyzing individually each pair of genes as correlation-based methods do. Model trees are a very useful technique to estimate the gene expression value by regression models and favours localized similarities over more global similarity, which is one of the major drawbacks of correlation-based methods. Here, we present an integrated software suite, named RegNetC , as a Cytoscape plugin that can operate on its own as well. RegNetC facilitates, according to user-defined parameters, the resulted transcriptional gene association network in .sif format for visualization, analysis and interoperates with other Cytoscape plugins, which can be exported for publication figures. In addition to the network, the RegNetC plugin also provides the quantitative relationships between genes expression values of those genes involved in the inferred network, i.e., those defined by the regression models.

Description: We introduce a new method for normalization of data acquired by liquid chromatography coupled with mass spectrometry (LC-MS) in label-free differential expression analysis. Normalization of LC-MS data is desired prior to subsequent statistical analysis to adjust variabilities in ion intensities that are not caused by biological differences but experimental bias. There are different sources of bias including variabilities during sample collection and sample storage, poor experimental design, noise, etc. In addition, instrument variability in experiments involving a large number of LC-MS runs leads to a significant drift in intensity measurements. Although various methods have been proposed for normalization of LC-MS data, there is no universally applicable approach. In this paper, we propose a Bayesian normalization model (BNM) that utilizes scan-level information from LC-MS data. Specifically, the proposed method uses peak shapes to model the scan-level data acquired from extracted ion chromatograms (EIC) with parameters considered as a linear mixed effects model. We extended the model into BNM with drift (BNMD) to compensate for the variability in intensity measurements due to long LC-MS runs. We evaluated the performance of our method using synthetic and experimental data. In comparison with several existing methods, the proposed BNM and BNMD yielded significant improvement.

Description: Performing clustering analysis is one of the important research topics in cancer discovery using gene expression profiles, which is crucial in facilitating the successful diagnosis and treatment of cancer. While there are quite a number of research works which perform tumor clustering, few of them considers how to incorporate fuzzy theory together with an optimization process into a consensus clustering framework to improve the performance of clustering analysis. In this paper, we first propose a random double clustering based cluster ensemble framework (RDCCE) to perform tumor clustering based on gene expression data. Specifically, RDCCE generates a set of representative features using a randomly selected clustering algorithm in the ensemble, and then assigns samples to their corresponding clusters based on the grouping results. In addition, we also introduce the random double clustering based fuzzy cluster ensemble framework (RDCFCE), which is designed to improve the performance of RDCCE by integrating the newly proposed fuzzy extension model into the ensemble framework. RDCFCE adopts the normalized cut algorithm as the consensus function to summarize the fuzzy matrices generated by the fuzzy extension models, partition the consensus matrix, and obtain the final result. Finally, adaptive RDCFCE (A-RDCFCE) is proposed to optimize RDCFCE and improve the performance of RDCFCE further by adopting a self-evolutionary process (SEPP) for the parameter set. Experiments on real cancer gene expression profiles indicate that RDCFCE and A-RDCFCE works well on these data sets, and outperform most of the state-of-the-art tumor clustering algorithms.

Description: Named-entity recognition (NER) plays an important role in the development of biomedical databases. However, the existing NER tools produce multifarious named-entities which may result in both curatable and non-curatable markers. To facilitate biocuration with a straightforward approach, classifying curatable named-entities is helpful with regard to accelerating the biocuration workflow. Co-occurrence Interaction Nexus with Named-entity Recognition (CoINNER) is a web-based tool that allows users to identify genes, chemicals, diseases, and action term mentions in the Comparative Toxicogenomic Database (CTD). To further discover interactions, CoINNER uses multiple advanced algorithms to recognize the mentions in the BioCreative IV CTD Track. CoINNER is developed based on a prototype system that annotated gene, chemical, and disease mentions in PubMed abstracts at BioCreative 2012 Track I (literature triage). We extended our previous system in developing CoINNER. The pre-tagging results of CoINNER were developed based on the state-of-the-art named entity recognition tools in BioCreative III. Next, a method based on conditional random fields (CRFs) is proposed to predict chemical and disease mentions in the articles. Finally, action term mentions were collected by latent Dirichlet allocation (LDA). At the BioCreative IV CTD Track, the best F-measures reached for gene/protein, chemical/drug and disease NER were 54 percent while CoINNER achieved a 61.5 percent F-measure. System URL: http://ikmbio.csie.ncku.edu.tw/coinner/introduction.htm.

Description: Next-generation short-read sequencing is widely utilized in genomic studies. Biological applications require an alignment step to map sequencing reads to the reference genome, before acquiring expected genomic information. This requirement makes alignment accuracy a key factor for effective biological interpretation. Normally, when accounting for measurement errors and single nucleotide polymorphisms, short read mappings with a few mismatches are generally considered acceptable. However, to further improve the efficiency of short-read sequencing alignment, we propose a method to retrieve additional reliably aligned reads (reads with more than a pre-defined number of mismatches), using a Bayesian-based approach. In this method, we first retrieve the sequence context around the mismatched nucleotides within the already aligned reads; these loci contain the genomic features where sequencing errors occur. Then, using the derived pattern, we evaluate the remaining (typically discarded) reads with more than the allowed number of mismatches, and calculate a score that represents the probability that a specific alignment is correct. This strategy allows the extraction of more reliably aligned reads, therefore improving alignment sensitivity. Implementation: The source code of our tool, ResSeq, can be downloaded from: https://github.com/hrbeubiocenter/Resseq.

Description: In genome assembly graphs, motifs such as tips, bubbles, and cross links are studied in order to find sequencing errors and to understand the nature of the genome. Superbubble, a complex generalization of bubbles, was recently proposed as an important subgraph class for analyzing assembly graphs. At present, a quadratic time algorithm is known. This paper gives an -time algorithm to solve this problem for a graph with $m$ edges.

Description: The papers in this special section focus on software and databases that are central in bioinformatics and computational biology.. These programs are playing more and more important roles in biology and medical research. These papers cover a broad range of topics, including computational genomics and transcriptomics, analysis of biological networks and interactions, drug design, biomedical signal/image analysis, biomedical text mining and ontologies, biological data mining, visualization and integration, and high performance computing application in bioinformatics.

Description: In the computational biology community, machine learning algorithms are key instruments for many applications, including the prediction of gene-functions based upon the available biomolecular annotations. Additionally, they may also be employed to compute similarity between genes or proteins. Here, we describe and discuss a software suite we developed to implement and make publicly available some of such prediction methods and a computational technique based upon Latent Semantic Indexing (LSI), which leverages both inferred and available annotations to search for semantically similar genes. The suite consists of three components. BioAnnotationPredictor is a computational software module to predict new gene-functions based upon Singular Value Decomposition of available annotations. SimilBio is a Web module that leverages annotations available or predicted by BioAnnotationPredictor to discover similarities between genes via LSI. The suite includes also SemSim , a new Web service built upon these modules to allow accessing them programmatically. We integrated SemSim in the Bio Search Computing framework (http://www.bioinformatics.deib.polimi.it/bio-seco/seco/), where users can exploit the Search Computing technology to run multi-topic complex queries on multiple integrated Web services. Accordingly, researchers may obtain ranked answers involving the computation of the functional similarity between genes in support of biomedical knowledge discovery.

Description: Identification of cancer subtypes plays an important role in revealing useful insights into disease pathogenesis and advancing personalized therapy. The recent development of high-throughput sequencing technologies has enabled the rapid collection of multi-platform genomic data (e.g., gene expression, miRNA expression, and DNA methylation) for the same set of tumor samples. Although numerous integrative clustering approaches have been developed to analyze cancer data, few of them are particularly designed to exploit both deep intrinsic statistical properties of each input modality and complex cross-modality correlations among multi-platform input data. In this paper, we propose a new machine learning model, called multimodal deep belief network (DBN), to cluster cancer patients from multi-platform observation data. In our integrative clustering framework, relationships among inherent features of each single modality are first encoded into multiple layers of hidden variables, and then a joint latent model is employed to fuse common features derived from multiple input modalities. A practical learning algorithm, called contrastive divergence (CD), is applied to infer the parameters of our multimodal DBN model in an unsupervised manner. Tests on two available cancer datasets show that our integrative data analysis approach can effectively extract a unified representation of latent features to capture both intra- and cross-modality correlations, and identify meaningful disease subtypes from multi-platform cancer data. In addition, our approach can identify key genes and miRNAs that may play distinct roles in the pathogenesis of different cancer subtypes. Among those key miRNAs, we found that the expression level of miR-29a is highly correlated with survival time in ovarian cancer patients. These results indicate that our multimodal DBN based data analysis approach may have practical applications in cancer pathogenesis studies and provide useful guidelines for personali- ed cancer therapy.

Description: The papers in this special issue contain extended versions of works that were originally presented at the Brazilian Symposium on Bioinformatics 2013 (BSB 2013), held in Recife, Brazil, November 3-6, 2013.

Description: Computational methods for predicting protein-protein interactions are important tools that can complement high-throughput technologies and guide biologists in designing new laboratory experiments. The proteins and the interactions between them can be described by a network which is characterized by several topological properties. Information about proteins and interactions between them, in combination with knowledge about topological properties of the network, can be used for developing computational methods that can accurately predict unknown protein-protein interactions. This paper presents a supervised learning framework based on Bayesian inference for combining two types of information: i) network topology information, and ii) information related to proteins and the interactions between them. The motivation of our model is that by combining these two types of information one can achieve a better accuracy in predicting protein-protein interactions, than by using models constructed from these two types of information independently.

Description: We develop a theory of algebraic operations over linear and context-free grammars that makes it possible to combine simple “atomic” grammars operating on single sequences into complex, multi-dimensional grammars. We demonstrate the utility of this framework by constructing the search spaces of complex alignment problems on multiple input sequences explicitly as algebraic expressions of very simple one-dimensional grammars. In particular, we provide a fully worked frameshift-aware, semiglobal DNA-protein alignment algorithm whose grammar is composed of products of small, atomic grammars. The compiler accompanying our theory makes it easy to experiment with the combination of multiple grammars and different operations. Composite grammars can be written out in $ {rm L}^AT_{E}X$ for documentation and as a guide to implementation of dynamic programming algorithms. An embedding in Haskell as a domain-specific language makes the theory directly accessible to writing and using grammar products without the detour of an external compiler. Software and supplemental files available here: http://www.bioinf.uni-leipzig.de/Software/gramprod/

Description: Recent advancements in genomics and proteomics provide a solid foundation for understanding the pathogenesis of diabetes. Proteomics of diabetes associated pathways help to identify the most potent target for the management of diabetes. The relevant datasets are scattered in various prominent sources which takes much time to select the therapeutic target for the clinical management of diabetes. However, additional information about target proteins is needed for validation. This lacuna may be resolved by linking diabetes associated genes, pathways and proteins and it will provide a strong base for the treatment and planning management strategies of diabetes. Thus, a web source “Diabetes Associated Proteins Database (DAPD)” has been developed to link the diabetes associated genes, pathways and proteins using PHP, MySQL. The current version of DAPD has been built with proteins associated with different types of diabetes. In addition, DAPD has been linked to external sources to gain the access to more participatory proteins and their pathway network. DAPD will reduce the time and it is expected to pave the way for the discovery of novel anti-diabetic leads using computational drug designing for diabetes management. DAPD is open accessed via following url www.mkarthikeyan.bioinfoau.org/dapd.

Description: Determining the glycan topology automatically from mass spectra represents a great challenge. Existing methods fall into approximate and exact ones. The former including greedy and heuristic ones can reduce the computational complexity, but suffer from information lost in the procedure of glycan interpretation. The latter including dynamic programming and exhaustive enumeration are much slower than the former. In the past years, nearly all emerging methods adopted a tree structure to represent a glycan. They share such problems as repetitive peak counting in reconstructing a candidate structure. Besides, tree-based glycan representation methods often have to give different computational formulas for binary and ternary glycans. We propose a new directed acyclic graph structure for glycan representation. Based on it, this work develops a de novo algorithm to accurately reconstruct the tree structure iteratively from mass spectra with logical constraints and some known biosynthesis rules, by a single computational formula. The experiments on multiple complex glycans extracted from human serum show that the proposed algorithm can achieve higher accuracy to determine a glycan topology than prior methods without increasing computational burden.

Description: A crucial step in understanding the architecture of cells and tissues from microscopy images, and consequently explain important biological events such as wound healing and cancer metastases, is the complete extraction and enumeration of individual filaments from the cellular cytoskeletal network. Current efforts at quantitative estimation of filament length distribution, architecture and orientation from microscopy images are predominantly limited to visual estimation and indirect experimental inference. Here we demonstrate the application of a new algorithm to reliably estimate centerlines of biological filament bundles and extract individual filaments from the centerlines by systematically disambiguating filament intersections. We utilize a filament enhancement step followed by reverse diffusion based filament localization and an integer programming based set combination to systematically extract accurate filaments automatically from microscopy images. Experiments on simulated and real confocal microscope images of flat cells (2D images) show efficacy of the new method.

Description: The Local/Global Alignment (Zemla, 2003), or LGA, is a popular method for the comparison of protein structures. One of the two components of LGA requires us to compute the longest common contiguous segments between two protein structures. That is, given two structures $A=(a_1, ldots , a_n)$ and $B=(b_1, ldots , b_n)$ where $a_k$ , $b_kin mathbb {R}^3$ , we are to find, among all the segments $f=(a_i,ldots ,a_j)$ and $g=(b_i,ldots ,b_j)$ that fulfill a certain criterion regarding their similarity, those of the maximum length. We consider the following criteria: (1) the root mean squared deviation (RMSD) between $f$ and $g$ is to be within a given $tin mathbb {R}$ ; (2) $f$ and $g$ can be superposed such that for each $k$ , $ile kle j$ , $Vert a_k-b_kVert le t$ for a given $tin mathbb {R}$ . We give an algorithm of $O(n;log; n+n{{boldsymbol l}})$ time complexity when the first requirement applies, where ${{boldsymbol l}}$ is the maximum length of the segments fulfilling the criterion. We show an FPTAS which, for any

Description: Noise can induce various dynamical behaviors in nonlinear systems. White noise perturbed systems have been extensively investigated during the last decades. In gene networks, experimentally observed extrinsic noise is colored. As an attempt, we investigate the genetic toggle switch systems perturbed by colored extrinsic noise and with kinetic parameters. Compared with white noise perturbed systems, we show there also exists optimal colored noise strength to induce the best stochastic switch behaviors in the single toggle switch, and the best synchronized switching in the networked systems, which demonstrate that noise-induced optimal switch behaviors are widely in existence. Moreover, under a wide range of system parameter regions, we find there exist wider ranges of white and colored noises strengths to induce good switch and synchronization behaviors, respectively; therefore, white noise is beneficial for switch and colored noise is beneficial for population synchronization. Our observations are very robust to extrinsic stimulus strength, cell density, and diffusion rate. Finally, based on the Waddington’s epigenetic landscape and the Wiener-Khintchine theorem, physical mechanisms underlying the observations are interpreted. Our investigations can provide guidelines for experimental design, and have potential clinical implications in gene therapy and synthetic biology.

Description: Revealing the underlying evolutionary mechanism plays an important role in understanding protein interaction networks in the cell. While many evolutionary models have been proposed, the problem about applying these models to real network data, especially for differentiating which model can better describe evolutionary process for the observed network remains a challenge. The traditional way is to use a model with presumed parameters to generate a network, and then evaluate the fitness by summary statistics, which however cannot capture the complete network structures information and estimate parameter distribution. In this work, we developed a novel method based on Approximate Bayesian Computation and modified Differential Evolution algorithm (ABC-DEP) that is capable of conducting model selection and parameter estimation simultaneously and detecting the underlying evolutionary mechanisms for PPI networks more accurately. We tested our method for its power in differentiating models and estimating parameters on simulated data and found significant improvement in performance benchmark, as compared with a previous method. We further applied our method to real data of protein interaction networks in human and yeast. Our results show duplication attachment model as the predominant evolutionary mechanism for human PPI networks and Scale-Free model as the predominant mechanism for yeast PPI networks.

Description: Single nucleotide polymorphisms, a dominant type of genetic variants, have been used successfully to identify defective genes causing human single gene diseases. However, most common human diseases are complex diseases and caused by gene-gene and gene-environment interactions. Many SNP-SNP interaction analysis methods have been introduced but they are not powerful enough to discover interactions more than three SNPs. The paper proposes a novel method that analyzes all SNPs simultaneously. Different from existing methods, the method regards an individual’s genotype data on a list of SNPs as a point with a unit of energy in a multi-dimensional space, and tries to find a new coordinate system where the energy distribution difference between cases and controls reaches the maximum. The method will find different multiple SNPs combinatorial patterns between cases and controls based on the new coordinate system. The experiment on simulated data shows that the method is efficient. The tests on the real data of age-related macular degeneration (AMD) disease show that it can find out more significant multi-SNP combinatorial patterns than existing methods.

Description: Disulfide connectivity is an important protein structural characteristic. Accurately predicting disulfide connectivity solely from protein sequence helps to improve the intrinsic understanding of protein structure and function, especially in the post-genome era where large volume of sequenced proteins without being functional annotated is quickly accumulated. In this study, a new feature extracted from the predicted protein 3D structural information is proposed and integrated with traditional features to form discriminative features. Based on the extracted features, a random forest regression model is performed to predict protein disulfide connectivity. We compare the proposed method with popular existing predictors by performing both cross-validation and independent validation tests on benchmark datasets. The experimental results demonstrate the superiority of the proposed method over existing predictors. We believe the superiority of the proposed method benefits from both the good discriminative capability of the newly developed features and the powerful modelling capability of the random forest. The web server implementation, called TargetDisulfide, and the benchmark datasets are freely available at: http://csbio.njust.edu.cn/bioinf/TargetDisulfide for academic use.

Description: Proteins are molecules that form the mass of living beings. These proteins exist in dissociated forms like amino-acids and carry out various biological functions, in fact, almost all body reactions occur with the participation of proteins. This is one of the reasons why the analysis of proteins has become a major issue in biology. In a more concrete way, the identification of conserved patterns in a set of related protein sequences can provide relevant biological information about these protein functions. In this paper, we present a novel algorithm based on teaching learning based optimization (TLBO) combined with a local search function specialized to predict common patterns in sets of protein sequences. This population-based evolutionary algorithm defines a group of individuals (solutions) that enhance their knowledge (quality) by means of different learning stages. Thus, if we correctly adapt it to the biological context of the mentioned problem, we can get an acceptable set of quality solutions. To evaluate the performance of the proposed technique, we have used six instances composed of different related protein sequences obtained from the PROSITE database. As we will see, the designed approach makes good predictions and improves the quality of the solutions found by other well-known biological tools.

Description: We introduce Supervised Variational Relevance Learning (Suvrel), a variational method to determine metric tensors to define distance based similarity in pattern classification, inspired in relevance learning. The variational method is applied to a cost function that penalizes large intraclass distances and favors small interclass distances. We find analytically the metric tensor that minimizes the cost function. Preprocessing the patterns by doing linear transformations using the metric tensor yields a dataset which can be more efficiently classified. We test our methods using publicly available datasets, for some standard classifiers. Among these datasets, two were tested by the MAQC-II project and, even without the use of further preprocessing, our results improve on their performance.

Description: Genes can participate in multiple biological processes at a time and thus their expression can be seen as a composition of the contributions from the active processes. Biclustering under a plaid assumption allows the modeling of interactions between transcriptional modules or biclusters (subsets of genes with coherence across subsets of conditions) by assuming an additive composition of contributions in their overlapping areas. Despite the biological interest of plaid models, few biclustering algorithms consider plaid effects and, when they do, they place restrictions on the allowed types and structures of biclusters, and suffer from robustness problems by seizing exact additive matchings. We propose BiP (Biclustering using Plaid models), a biclustering algorithm with relaxations to allow expression levels to change in overlapping areas according to biologically meaningful assumptions (weighted and noise-tolerant composition of contributions). BiP can be used over existing biclustering solutions (seizing their benefits) as it is able to recover excluded areas due to unaccounted plaid effects and detect noisy areas non-explained by a plaid assumption, thus producing an explanatory model of overlapping transcriptional activity. Experiments on synthetic data support BiP’s efficiency and effectiveness. The learned models from expression data unravel meaningful and non-trivial functional interactions between biological processes associated with putative regulatory modules.

Description: We propose a classifier system called iPFPi that predicts the functions of un-annotated proteins. iPFPi assigns an un-annotated protein $P$ the functions of GO annotation terms that are semantically similar to $P$ . An un-annotated protein $P$ and a GO annotation term $T$ are represented by their characteristics. The characteristics of $P$ are GO terms found within the abstracts of biomedical literature associated with $P$ . The characteristics of $T$ are GO terms found within the abstracts of biomedical literature associated with the proteins annotated with the function of $T$ . Let - F$ and $Fprime $ be the important (dominant) sets of characteristic terms representing $T$ and $P$ , respectively. iPFPi would annotate $P$ with the function of $T$ , if $F$ and $Fprime $ are semantically similar. We constructed a novel semantic similarity measure that takes into consideration several factors, such as the dominance degree of each characteristic term $t$ in set $F$

Description: Adverse drug reaction (ADR) is a common clinical problem, sometimes accompanying with high risk of mortality and morbidity. It is also one of the major factors that lead to failure in new drug development. Unfortunately, most of current experimental and computational methods are unable to evaluate clinical safety of drug candidates in early drug discovery stage due to the very limited knowledge of molecular mechanisms underlying ADRs. Therefore, in this study, we proposed a novel naïve Bayesian model for rapid assessment of clinical ADRs with frequency estimation. This model was constructed on a gene-ADR association network, which covered 611 US FDA approved drugs, 14,251 genes, and 1,254 distinct ADR terms. An average detection rate of 99.86 and 99.73 percent were achieved eventually in identification of known ADRs in internal test data set and external case analyses respectively. Moreover, a comparative analysis between the estimated frequencies of ADRs and their observed frequencies was undertaken. It is observed that these two frequencies have the similar distribution trend. These results suggest that the naïve Bayesian model based on gene-ADR association network can serve as an efficient and economic tool in rapid ADRs assessment.

Description: The papers in this special section were presented at the 13th International Workshop on Data Mining in Bioinformatics (BIOKDD???14) was organized in conjunction with the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining that was held on August 24, 2014 in New York, NY. It brought together international researchers in the interacting disciplines of data mining, systems biology, and bioinformatics at the Bloomberg Headquarters venue. The goal of this workshop is to encourage Knowledge Discovery and Data mining (KDD) researchers to take on the numerous challenges that Bioinformatics offers.

Description: The introduction of next-generation sequencing technologies has radically changed the way we view structural genetic events. Microhomology-mediated break-induced replication (MMBIR) is just one of the many mechanisms that can cause genomic destabilization that may lead to cancer. Although the mechanism for MMBIR remains unclear, it has been shown that MMBIR is typically associated with template-switching events. Currently, to our knowledge, there is no existing bioinformatics tool to detect these template-switching events. We have developed MMBIRFinder, a method that detects template-switching events associated with MMBIR from whole-genome sequenced data. MMBIRFinder uses a half-read alignment approach to identify potential regions of interest. Clustering of these potential regions helps narrow the search space to regions with strong evidence. Subsequent local alignments identify the template-switching events with single-nucleotide accuracy. Using simulated data, MMBIRFinder identified 83 percent of the MMBIR regions within a five nucleotide tolerance. Using real data, MMBIRFinder identified 16 MMBIR regions on a normal breast tissue data sample and 51 MMBIR regions on a triple-negative breast cancer tumor sample resulting in detection of 37 novel template-switching events. Finally, we identified template-switching events residing in the promoter region of seven genes that have been implicated in breast cancer. The program is freely available for download at https://github.com/msegar/MMBIRFinder.

Description: Compressing heterogeneous collections of trees is an open problem in computational phylogenetics. In a heterogeneous tree collection, each tree can contain a unique set of taxa. An ideal compression method would allow for the efficient archival of large tree collections and enable scientists to identify common evolutionary relationships over disparate analyses. In this paper, we extend TreeZip to compress heterogeneous collections of trees. TreeZip is the most efficient algorithm for compressing homogeneous tree collections. To the best of our knowledge, no other domain-based compression algorithm exists for large heterogeneous tree collections or enable their rapid analysis. Our experimental results indicate that TreeZip averages 89.03 percent (72.69 percent) space savings on unweighted (weighted) collections of trees when the level of heterogeneity in a collection is moderate. The organization of the TRZ file allows for efficient computations over heterogeneous data. For example, consensus trees can be computed in mere seconds. Lastly, combining the TreeZip compressed (TRZ) file with general-purpose compression yields average space savings of 97.34 percent (81.43 percent) on unweighted (weighted) collections of trees. Our results lead us to believe that TreeZip will prove invaluable in the efficient archival of tree collections, and enables scientists to develop novel methods for relating heterogeneous collections of trees.

Description: The papers in this special section were presented at the 2014 International Conference on Genome Informatics (GIW).

Description: Cluster analysis of biological networks is one of the most important approaches for identifying functional modules and predicting protein functions. Furthermore, visualization of clustering results is crucial to uncover the structure of biological networks. In this paper, ClusterViz, an APP of Cytoscape 3 for cluster analysis and visualization, has been developed. In order to reduce complexity and enable extendibility for ClusterViz, we designed the architecture of ClusterViz based on the framework of Open Services Gateway Initiative. According to the architecture, the implementation of ClusterViz is partitioned into three modules including interface of ClusterViz, clustering algorithms and visualization and export. ClusterViz fascinates the comparison of the results of different algorithms to do further related analysis. Three commonly used clustering algorithms, FAG-EC, EAGLE and MCODE, are included in the current version. Due to adopting the abstract interface of algorithms in module of the clustering algorithms, more clustering algorithms can be included for the future use. To illustrate usability of ClusterViz, we provided three examples with detailed steps from the important scientific articles, which show that our tool has helped several research teams do their research work on the mechanism of the biological networks.

Description: Structural domains are evolutionary and functional units of proteins and play a critical role in comparative and functional genomics. Computational assignment of domain function with high reliability is essential for understanding whole-protein functions. However, functional annotations are conventionally assigned onto full-length proteins rather than associating specific functions to the individual structural domains. In this article, we present Structural Domain Annotation (SDA), a novel computational approach to predict functions for SCOP structural domains. The SDA method integrates heterogeneous information sources, including structure alignment based protein-SCOP mapping features, InterPro2GO mapping information, PSSM Profiles, and sequence neighborhood features, with a Bayesian network. By large-scale annotating Gene Ontology terms to SCOP domains with SDA, we obtained a database of SCOP domain to Gene Ontology mappings, which contains $sim$ 162,000 out of the approximately 166,900 domains in SCOPe 2.03 ( $>$ 97 percent) and their predicted Gene Ontology functions. We have benchmarked SDA using a single-domain protein dataset and an independent dataset from different species. Comparative studies show that SDA significantly outperforms the existing function prediction methods for structural domains in terms of coverage and maximum F-measure.

Description: A major challenge in computational biology is to find simple representations of high-dimensional data that best reveal the underlying structure. In this work, we present an intuitive and easy-to-implement method based on ranked neighborhood comparisons that detects structure in unsupervised data. The method is based on ordering objects in terms of similarity and on the mutual overlap of nearest neighbors. This basic framework was originally introduced in the field of social network analysis to detect actor communities. We demonstrate that the same ideas can successfully be applied to biomedical data sets in order to reveal complex underlying structure. The algorithm is very efficient and works on distance data directly without requiring a vectorial embedding of data. Comprehensive experiments demonstrate the validity of this approach. Comparisons with state-of-the-art clustering methods show that the presented method outperforms hierarchical methods as well as density based clustering methods and model-based clustering. A further advantage of the method is that it simultaneously provides a visualization of the data. Especially in biomedical applications, the visualization of data can be used as a first pre-processing step when analyzing real world data sets to get an intuition of the underlying data structure. We apply this model to synthetic data as well as to various biomedical data sets which demonstrate the high quality and usefulness of the inferred structure.

Description: Proline residues are common source of kinetic complications during folding. The X-Pro peptide bond is the only peptide bond for which the stability of the cis and trans conformations is comparable. The cis-trans isomerization (CTI) of X-Pro peptide bonds is a widely recognized rate-limiting factor, which can not only induces additional slow phases in protein folding but also modifies the millisecond and sub-millisecond dynamics of the protein. An accurate computational prediction of proline CTI is of great importance for the understanding of protein folding, splicing, cell signaling, and transmembrane active transport in both the human body and animals. In our earlier work, we successfully developed a biophysically motivated proline CTI predictor utilizing a novel tree-based consensus model with a powerful metalearning technique and achieved 86.58 percent Q2 accuracy and 0.74 Mcc, which is a better result than the results (70-73 percent Q2 accuracies) reported in the literature on the well-referenced benchmark dataset. In this paper, we describe experiments with novel randomized subspace learning and bootstrap seeding techniques as an extension to our earlier work, the consensus models as well as entropy-based learning methods, to obtain better accuracy through a precise and robust learning scheme for proline CTI prediction.

Description: Efficient search algorithms for finding genomic-range overlaps are essential for various bioinformatics applications. A majority of fast algorithms for searching the overlaps between a query range (e.g., a genomic variant) and a set of N reference ranges (e.g., exons) has time complexity of O ( k + log N ), where k denotes a term related to the length and location of the reference ranges. Here, we present a simple but efficient algorithm that reduces k, based on the maximum reference range length. Specifically, for a given query range and the maximum reference range length, the proposed method divides the reference range set into three subsets: always , potentially , and never overlapping . Therefore, search effort can be reduced by excluding never overlapping subset. We demonstrate that the running time of the proposed algorithm is proportional to potentially overlapping subset size, that is proportional to the maximum reference range length if all the other conditions are the same. Moreover, an implementation of our algorithm was 13.8 to 30.0 percent faster than one of the fastest range search methods available when tested on various genomic-range data sets. The proposed algorithm has been incorporated into a disease-linked variant prioritization pipeline for WGS (http://gnome.tchlab.org) and its implementation is available at http://ml.ssu.ac.kr/gSearch.

Description: The identification of protein complexes in protein-protein interaction (PPI) networks is fundamental for understanding biological processes and cellular molecular mechanisms. Many graph computational algorithms have been proposed to identify protein complexes from PPI networks by detecting densely connected groups of proteins. These algorithms assess the density of subgraphs through evaluation of the sum of individual edges or nodes; thus, incomplete and inaccurate measures may miss meaningful biological protein complexes with functional significance. In this study, we propose a novel method for assessing the compactness of local subnetworks by measuring the number of three node cliques. The present method detects each optimal cluster by growing a seed and maximizing the compactness function. To demonstrate the efficacy of the new proposed method, we evaluate its performance using five PPI networks on three reference sets of yeast protein complexes with five different measurements and compare the performance of the proposed method with four state-of-the-art methods. The results show that the protein complexes generated by the proposed method are of better quality than those generated by four classic methods. Therefore, the new proposed method is effective and useful for detecting protein complexes in PPI networks.

Description: Provides a listing of the editors, board members, and current staff for this issue of the publication.

Description: Nowadays, clean renewable energy extraction solutions are becoming a crucial practice in society. Many different sources are being developed including ocean energy and in specific, ocean waves. In deep water conditions, ocean waves can become very power dense, continuous, and forecastable. Wave height, velocity, and frequency are all variable wave characteristics making it challenging to capture wave power economically. The RTI F2 is a promising wave energy conversion device that is currently under research. Its method of power capture is a buoyant vessel oriented normal to oncoming waves. This paper discusses known control methods implemented on the RTI F2, the experimental setup used for control of the device, and wave tank testing done at the University of New Hampshire's Chase Laboratory. Experimental data was obtained across various wave conditions, plate angles, vessel weights, and control strategies. The results of these tests are presented in the subject matter.

Description: Many existing solar irradiance monitoring networks were built particularly for resource assessment purposes; they are often spatially sparse. In order for the networks to handle other increasingly important tasks, such as irradiance forecasting for grid integration, their spatial sparsity must be addressed by adding in new monitoring stations. Optimally expanding these networks using historical information thus becomes an important research topic for engineers. Variability of solar irradiance in space and time can be quantified using statistics such as entropy and covariance. The deployment of the additional monitoring stations should, therefore, utilize these statistics to reduce the variability. More specifically, we aim at maximizing the entropy of the network. A practical difficulty in statistical modeling of solar irradiance is that the data are not ideal. Properties such as stationarity and isotropy are not observed in irradiance random field. We, therefore, focus on hypothesis testing and transformation of the irradiance data, so that the design procedure is statistically justified. We propose the redesign framework in a solar engineering context, using data from 24 irradiance monitoring stations on a tropical island. In the case study, we demonstrate how to find three optimal stations from a pool of 100 potential future monitoring sites.

Description: This paper proposes a novel forecasting model based on a mean trend detector (MTD) and a mathematical morphology-based local predictor (MMLP) to undertake short-term forecast of wind power generation. In the proposed MTD/MMLP model, the nonstationary time series describing wind power generation is first decomposed by the MTD, which employs some new notions and conventional morphological operators. The decomposition yields two componentsthe mean trend, which reveals the tendency of the time series, and the stochastic component, which depicts the fluctuations caused by high frequency of the variability. Subsequently, the $p$ -step forecast is conducted for these two components separately. The mean trend is forecasted on the basis of the least-square support vector machine (LS-SVM) model, while the $p$ -step forecast for the stochastic component is carried out by the MMLP, which involves performing morphological operations employing a novel structuring element (SE) in the phase space. Finally, the forecast of wind power generation is achieved by combining the separate forecasts of two components. In order to evaluate the accuracy and stability of the MTD/MMLP model, simulation studies are carried out using the data obtained from three widely used databases sampled in different periods. The results demonstrate that the MTD/MMLP model provides a more accurate and stable forecast compared to the traditional methods.

Description: The distribution network planning under active network management (ANM) schemes is becoming of interest due to substantial benefits in facilitating the increasing integration of renewable energy sources. This paper presents various potential ANM schemes based on the photovoltaic inverter control (PVIC) considering enhanced utilization of the inverter reactive power capability. Depending on the active power generation of PV arrays, inverter size and desired reactive power settings, several PVIC schemes are proposed. The PVIC schemes are incorporated in the optimal power flow (OPF) and formulated as a nonlinear programming (NLP) problem. In this study, the PVIC schemes are applied to maximize the total wind-distributed generation (DG) penetration on a typical U.K. distribution system. Various case studies are presented and compared to evaluate the performance. The results show that the proposed schemes can significantly increase the wind penetration levels by 45.4% and up to 92.3%.

Description: In this paper, a health monitoring method for photovoltaic (PV) systems based on probabilistic neural network (PNN) is proposed that detects and classifies short- and open-circuit faults in real time. To implement and validate the proposed method in computer programs, a new approach for modeling PV systems is proposed that only requires information from manufacturers datasheet reported under normal-operating cell temperature (NOCT) conditions and standard-operating test conditions (STCs). The proposed model precisely represents characteristics of PV systems at different temperatures, as the temperature dependency of parameters such as ideality factor, series resistance, and thermal voltage is considered in the proposed model. Although this model can be applied to a variety of applications, it is specifically used to test and validate the performance of the proposed fault detection and classification method.

Description: Intermittent generation from wind farms leads to fluctuating power system operating conditions pushing the stability margin to its limits. The traditional way of determining the worst case generation dispatch for a system with several semi-scheduled wind generators yields a conservative solution. This paper proposes a fast estimation of the transient stability margin (TSM) incorporating the uncertainty of wind generation. First, the Kalman filter (KF) is used to provide linear estimation of system angle and then unscented transformation (UT) is used to estimate the distribution of the TSM. The proposed method is compared with the traditional Monte Carlo (MC) method and the effectiveness of the proposed approach is verified using Single Machine Infinite Bus (SMIB) and IEEE 14 generator Australian dynamic system. This method will aid grid operators to perform fast online calculations to estimate TSM distribution of a power system with high levels of intermittent wind generation.

Description: This paper presents a probabilistic-based approach for available transfer capability (ATC) assessment. A composite algorithm is developed to generate ensembles of future wind generation scenarios for the existing and planned wind sites using both measured and model-produced wind data. Then, the ensembles of wind and load are used to calculate their respective probability density functions (pdfs), which are subsequently used to calculate the probabilistic-based ATC for a selected transmission corridor. The method has been tested and validated using historical and operational data provided by the Idaho Power Co. The results show that the method can effectively quantify the uncertainties in the ATC assessment introduced by variable generation resources and load variations. As a result, the grid planners will inform the likelihood for the transmission corridor to exceed its transfer capacity in any targeted future years as well as the duration of such events.

Description: With the increasing size of wind farms, the impact of the wake effect on wind farm energy yields become more and more evident. The arrangement of locations of the wind turbines (WTs) will influence the capital investment and contribute to the wake losses, which incur the reduction of energy production. As a consequence, the optimized placement of the WTs may be done by considering the wake effect as well as the components cost within the wind farm. In this paper, a mathematical model which includes the variation of both wind direction and wake deficit is proposed. The problem is formulated by using levelized production cost (LPC) as the objective function. The optimization procedure is performed by a particle swarm optimization (PSO) algorithm with the purpose of maximizing the energy yields while minimizing the total investment. The simulation results indicate that the proposed method is effective to find the optimized layout, which minimizes the LPC. The optimization procedure is applicable for optimized placement of WTs within wind farms and extendible for different wind conditions and capacity of wind farms.

Description: This paper proposes a novel short-term wind power forecasting approach by mining the bad data of numerical weather prediction (NWP). Today's short-term wind power forecast (WPF) highly depends on the NWP, which contributes the most in the WPF error. This paper first introduces a bad data analyzer to fully study the relationship between the WPF error with several new extracted features from the raw NWP. Second, a hierarchical structure is proposed, which is composed of a K -means clustering-based bad data detection module and a neural network (NN)-based forecasting module. In the NN module, the WPF is fully adjusted based on the output of the bad data analyzer. Simulations are performed comparing with two other different methods. It proves that the proposed approach can improve the short-term wind power forecasting by effectively identifying and adjusting the errors from NWP.

Description: The objective of this study was to develop a reduced-order small-signal model of a microgrid system capable of operating in both the grid-tied and the islanded conditions. The nonlinear equations of the proposed system were derived in the $dq$ reference frame and then linearized around stable operating points to construct a small-signal model. The high-order state matrix was then reduced using the singular perturbation technique. The dynamic equations were divided into two groups based on the small-signal model parameters $varepsilon$ . The slow states, which dominated the systems dynamics, were preserved, whereas the fast states were eliminated. Step responses of the model were compared to the experimental results from a hardware test to assess their accuracy and similarity to the full-order system. The proposed reduced-order model was applied to a modified IEEE-37 bus grid-tied microgrid system to evaluate systems dynamic response in grid-tied mode, islanded mode, and transition from grid-tied to islanded mode.

Description: Many benefits can be achieved through the implementation of a Microgrid controller, such as minimized cost, reduction in peak power, power smoothing, greenhouse gas emission reduction, and increased reliability of service. However, most Microgrid controllers found in the literature and in the industry optimize a single objective, which either exacerbates or does not solve the problems with integrating a high penetration of renewable energy. This paper presents a methodology of formulating a multiobjective optimization (MOO) so that each objective is quantified through valuation functions that can be specific to every Microgrid. The proposed approach attains a Pareto-optimal solution by directly comparing the quantified valuation functions and solving as if it were a single-objective optimization (SOO) problem. Three cases of controllers are presented and compared: 1)a base case system with no controller; 2)an SOO that optimizes the cost of energy; and 3)an MOO that optimizes five identified benefits. Results show that the proposed controller can mitigate the negative impacts of volatile generation to levels below that of the system load.

Description: Variable over voltage, excessive tap counts, and voltage regulator (VR) runaway condition are major operational challenges in distribution network while accommodating generation from photovoltaics (PVs). The conventional approach to achieve voltage control based on offline simulation for voltage set point calculation does not consider forecast errors. In this work, a stochastic optimal voltage control strategy is proposed while considering load and irradiance forecast errors. Stochastic operational risks such as overvoltage and VR runaway are defined through a chance constrained optimization (CCO) problem. This classical formulation to mitigate runaway is further improved by introducing a stochastic index called the Tap Tail Expectation . Operational objectives such as power losses and excessive tap count minimization are considered in the formulation. A sampling approach is proposed to solve the CCO. Along with other voltage control devices, the PV inverter voltage support features are coordinated. The simulation study is performed using a realistic distribution system model and practically measured irradiance to demonstrate the effectiveness of the proposed technique. The proposed approach is a useful operational procedure for distribution system operators. The approach can minimize feeder power losses, avoid voltage violations, and alleviate VR runaway.

Description: The electromagnetic stability issues of the grid-connected doubly fed induction generator (DFIG) system are usually overlooked. This paper presents a reduced order small-signal model that can be used to analyze the stability of DFIGs dc-link voltage control system, especially under weak ac grid conditions. This model neglects DFIG flux and fast current control dynamics. However, the effects of operating points, grid strengths and control loops interactions on system dynamic performance are taken into account. An eigenvalue comparison shows the proposed model holds dominant oscillation mode featured by the detailed model and is suitable for stability analysis of dc-link voltage control system of DFIG. Influence coefficients reflecting control loops interactions are also presented. Application studies of the proposed model show it is suitable for illustrating the effect of grid strength on dynamic performance of the DFIGs dc-link voltage control system. Meanwhile, phase-locked loop (PLL) and rotor-side converter (RSC) active power control (APC)/reactive power controls (RPC) effect on system stability are also explored.

Description: Photovoltaic (PV) generation is increasingly popular in power systems. The nonlinear dependence associated with a large number of distributed PV sources adds the complexity to construct an accurate probability model and negatively affects confidence levels and reliability, thereby resulting in a more challenging operation of the systems. Most probability models have many restrictions when constructing multiple PV sources with complex dependence. This paper proposes a versatile probability model of PV generation on the basis of pair copula construction. In order to tackle the computational burden required to construct pair copula in high-dimensional cases, a systematic simplification technique is utilized that can significantly reduce the computational effort while preserving satisfactory precision. The proposed method can simplify the modeling procedure and provide a flexible and optimal probability model for the PV generation with complex dependence. The proposed model is tested using a set of historical data from colocated PV sites. It is then applied to the probabilistic load flow (PLF) study of the IEEE 118-bus system. The results demonstrate the effectiveness and accuracy of the proposed model.

Description: It is important to forecast the wind speed for managing operations in wind power plants. However, wind speed prediction is extremely complex and difficult due to the volatility and deviation of the wind. As existing forecasting methods directly model the raw wind speed data, it is difficult for them to provide higher inference accuracy. Differently, this paper presents a sophisticated deep-learning technique for short-term and long-term wind speed forecast, i.e., the predictive deep Boltzmann machine (PDBM) and corresponding learning algorithm. The proposed deep model forecasts wind speed by analyzing the higher level features abstracted from lower level features of the wind speed data. These automatically learnt features are very informative and appropriate for the prediction. The proposed PDBM is a deep stochastic model that can represent the wind speed very well, and is inspired by two aspects. 1)The stochastic model is suitable to capture the probabilistic characteristics of wind speed. 2)Recent developments in neural networks with deep architectures show that deep generative models have competitive capability to approximate nonlinear and nonsmooth functions. The evaluation of the proposed PDBM model is depicted by both hour-ahead and day-ahead prediction experiments based on real wind speed datasets. The prediction accuracy of the PDBM model outperforms existing methods by more than 10%.

Description: This paper presents a new maximum power point tracking (MPPT) method for photovoltaic (PV) systems. The proposed method improves the working of the conventional perturb and observe (P&O) method in changing environmental conditions by using the fractional short-circuit current (FSCC) method. It takes the initial operating point of a PV system by using the short-circuit current method and later shifts to the conventional P&O technique. The advantage of having this two-stage algorithm is rapid tracking under changing environmental conditions. In addition, this scheme offers low-power oscillations around MPP and, therefore, more power harvesting compared with the common P&O method. The proposed MPPT decides intelligently about the moment of measuring short-circuit current and is, therefore, an irradiance sensorless scheme. The proposed method is validated with computer software simulation followed by a dSPACE DS1104-based experimental setup. A buck-boost dc-dc converter is used for simulation and experimental confirmation. Furthermore, the reliability of the proposed method is also calculated. The results show that the proposed MPPT technique works satisfactorily under given environmental scenarios.

Description: In this paper, hardware-in-the-loop (HIL) implementation of solar photovoltaic (PV) array feeding autonomous load, without dump load, is investigated. Two control algorithms based on the sliding mode approach are designed to guarantee a fast and finite-time convergence without adjustment of the system parameters. The dc-dc boost converter and the current controlled-voltage source converter (CC-VSC) are controlled to maximize the power from the PV, to protect the battery energy storage system (BESS) from overcharging, and to regulate the voltage and frequency at the point of common coupling (PCC). An accurate stability analysis of the system is presented and discussed in this work. The effectiveness and the robustness of the developed controllers are validated by simulation and experimental results during the load perturbation and varying climate conditions.

Description: This paper proposes to use discrete Fourier transform (DFT) and discrete wavelet transform (DWT) methods to schedule grid-scale energy storage systems to mitigate wind power forecast error impacts while considering energy storage properties. This is accomplished by decomposing the wind forecast error signal to different time-varying periodic components to schedule sodium sulfur (NaS) batteries, compressed air energy storage (CAES), and conventional generators. The advantage of signal processing techniques is that the resultant decomposed components are appropriate for cycling of each energy storage technology. It is also beneficial for conventional generators, which are more efficient to operate close to rated capacity. The tradeoff between installing more energy storage units and decreasing the wind spillage, back-up energy, and the standard deviation of residual forecast error signal is analyzed. The NaS battery life cycle analysis and CAES contribution on increasing NaS battery lifetime are studied. The impact of considering the frequency bias constant to allow small frequency deviations is also investigated. To showcase the applicability of the proposed approach, a simulation case study based on a real-world 5-min interval wind data from Bonneville Power Administration (BPA) in 2013 is presented.

Description: One role of grid operators is to identify potential problems before they occur and, if necessary, take preemptive actions. As wind generation becomes increasingly widespread, there is the potential for credible, simultaneous fluctuations of output at different locations to result in limit violations. Existing analysis methods that consider forecast errors either inadequately model the control responses available to system operators (e.g., using participation factors) or ignore network constraints, which limits their utility in identifying situations that would require operator action. An alternative method of handling forecast errors, utilizing bilevel programming, is proposed here to identify situations that may result in branch overloads. What distinguishes this method from prior approaches is that it only identifies overloads that can occur despite optimal operator reaction to forecast errors--i.e., when the overload is unavoidable, given current control capabilities. Studies conducted on 37- and 118-bus test systems demonstrate both the utility and feasibility of using this method for online operations.

Description: In this paper, a control strategy for the participation of photovoltaic (PV) systems in frequency regulation is suggested. A number of strings from every inverter of a PV system are kept as reserve by disconnecting them through dc-relays. Hence, as the control algorithm monitors the grid frequency, it reconnects or disconnects strings, according to the occurring frequency deviations (negative and positive, respectively). Contrary to previous approaches, the suggested methodology here avoids the use of storage devices, which implies additional investment costs, and/or the manipulation of the maximum power point tracking (MPPT) algorithm of the inverter, which represents higher control complexity and special considerations depending on each algorithm. Simulation results from frequency phenomena and solar irradiation changes on a two-bus system in MATLAB Simulink are presented to show the favorable behavior and effective performance of the proposed control strategy. The design concept is also experimentally tested under various operating conditions and on different devices; the results also confirm the feasibility and simplicity of the method.

Description: In case of abnormal conditions, distribution systems should be reconfigured to overcome the impacts of outages such as overloads of network components and increased power losses. For this purpose, energy storage systems (ESS) and renewable energy sources (RES) can be applied to improve operating conditions. An optimal contingency assessment model using two-stage stochastic linear programming including wind power generation and a generic ESS is presented. The optimization model is applied to find the best radial topology by determining the best switching sequence to solve contingencies. The proposed model is applied to a 69-node distribution system and the results of all possible contingencies in the network are examined considering three different case studies with several scenarios. In addition, a reconfiguration analysis including all the contingencies is presented for the case studies.

Description: Ocean energy is a promising resource for renewable electricity generation that presents many advantages, such as being more predictable than wind energy, but also some disadvantages such as large and slow amplitude variations in the generated power. This paper presents a hardware-in-the-loop prototype that allows the study of the electric power profile generated by a wave power plant based on the oscillating water column (OWC) principle. In particular, it facilitates the development of new solutions to improve the intermittent profile of the power fed into the grid or the test of the OWC behavior when facing a voltage dip. Also, to obtain a more realistic model behavior, statistical models of real waves have been implemented.

Description: With the significant penetration of wind generation, the variability and uncertainty of wind energy poses new challenges to power system operations. In particular, more rapid reserve is required, which may result in the scarcity of balancing services. With the increasing penetration of renewable generation, it is envisaged that renewable resources will be required to partake in the system balancing tasks. In this paper, a combined flexible dispatch and reserve scheduling policy is proposed by determining a flexible wind dispatch margin. In order to provide a flexible dispatch margin, wind generators underschedule in the hour-ahead energy market, so as to hold some expected output for reserves. Additional wind energy is then available for mitigating forecast errors and other system uncertainties. This paper presents a framework to find the optimal policy to incorporate the flexible wind dispatch margin into the hour-ahead market. A finite-state Markov chain wind power forecast model, based on spatio-temporal analysis, is utilized. The presented framework is used to find the appropriate level of wind dispatch margin. The proposed approach is tested and the wind generation data are used to analyze the effectiveness of the presented model in coping with forecast errors and achieving a more secure system operation.

Description: A megawatt (MW)-scale hydro-viscous transmission-based continuously variable speed wind turbine is proposed to guarantee a smooth transition among different operating regions and hence to improve power efficiency and quality. This turbine is achieved by highly integrating a hydro-viscous element into the turbine drive-train to mitigate the upstream wind-loading fluctuations. This element allows the turbine speed to be directly regulated by continuously changing the oil film thickness in this element. Three important operating modes of this turbine system are proposed. The control-oriented drive-train model is also established and validated based on experimental data. A cooperative control strategy over the full operating range is then proposed based on such modes. A series of comparative cosimulations are carried out to evaluate the stability and effectiveness of the proposed turbine system in speed and power regulations. This proposed system holds several advantages such as large power capacity, high efficiency, downsized power converters, and low cost. Such advantages make this turbine system particularly attractive and promising for medium-to-large-scale wind power applications.

Description: In this paper, a novel ensemble method consisting of neural networks, wavelet transform, feature selection, and partial least-squares regression (PLSR) is proposed for the generation forecasting of a wind farm. Based on the conditional mutual information, a feature selection technique is developed to choose a compact set of input features for the forecasting model. In order to overcome the nonstationarity of wind power series and improve the forecasting accuracy, a new wavelet-based ensemble scheme is integrated into the model. The individual forecasters are featured with different mixtures of the mother wavelet and the number of decomposition levels. The individual outputs are combined to form the ensemble forecast output using the PLSR method. To confirm the effectiveness, the proposed method is examined on real-world datasets and compared with other forecasting methods.

Description: An optimal reactive power dispatch strategy is proposed to minimize the total electrical losses of a wind farm (WF), including not only losses in the transmission cables and wind turbine (WT) transformers, but also losses inside wind energy generation systems. The reactive power dispatch inside a WT uses optimal splitting strategy over the stator and the grid side converter (GSC), which aims to minimize the total loss of the wind energy generation system, including the generator, the converters, and the filters. Optimization problems are formulated based on established loss models and WT reactive power limits. A WF is carefully designed and used for case studies. Wake effect is considered when calculating the active power at each WT. The total losses of the WF are calculated by implementing the proposed strategy at different wind speeds and reactive power references. The simulation results show the effectiveness of the proposed strategy.

Description: Multibody wave energy converters are composed of several bodies interconnected by joints. Two different formulations are adopted to describe the dynamics of multibody systems: the differential and algebraic equations (DAEs) formulation, and the ordinary differential equations (ODEs) formulation. While the number of variables required for the description of the dynamics of a multibody system is greater in the DAE formulation than in the ODE formulation, the ODE formulation involves an extra computational effort in order to describe the dynamics of the system with a smaller number of variables. In this paper, pseudo-spectral (PS) methods are applied in order to solve the dynamics of multibody wave energy converters using both DAE and ODE formulations. Apart from providing a solution to the dynamics of multibody systems, pseudo-spectral methods provide an accurate and efficient formulation for the control of multibody wave energy converters. As an application example, this paper focuses on the dynamic modeling of a three-body hinge-barge device, where wave-tank tests are carried out in order to validate the DAE and ODE models against experimental data. Comparison of the ODE and DAE PS methods against a reference model based on the straightforward (Runge-Kutta) integration of the equations of motion shows that pseudo-spectral methods are computationally more stable and require less computational effort for short time steps.

Description: Optimal and simultaneous siting and sizing of distributed generators and capacitor banks in distribution systems have attracted a lot of attention from distribution companies. The placement and capacity of these devices have direct effects on the system’s performance. This paper presents a model for the simultaneous allocation of capacitor banks and distributed generation, which takes into account the stochastic nature of distributed generation. To solve the model presented, we propose an efficient hybrid method based on Tabu search and genetic algorithms. The hybrid method is applied to a well-known system in literature.

Description: In this paper, distributed energy storage (DES) devices, like batteries and ultra-capacitors, are used to alleviate detrimental impacts of high penetration photovoltaic (PV) resources on distribution systems. The impacts are studied at mainly two time resolutions—one minute and one hour. To determine accurately the size of the required DES for the purpose of mitigating the impacts of large-scale distributed PV, sizing procedures based on OpenDSS are proposed. After determining the total size of the required DES, optimization techniques can be used to choose the optimal locations for the DES along the feeder, which is a continuous optimization problem taking into account equality constraints of the AC power flow. The continuity of the problem and the radial network structure make it possible to apply a convex optimization technique called second order cone programming (SOCP) relaxation to obtain the globally optimal solution and avoid the problem of NP-hardness. The exactness of the introduced SOCP relaxation is sensitive to the chosen objective function and additional quadratic equalities. The necessary and sufficient condition of exactness for the SOCP relaxation of the DES optimal allocation and operation in radial distribution systems is studied. The proposed methods are applied to an actual feeder in the southwestern US with high penetration of PV using actual measured data. The simulation results demonstrate the efficacy of the proposed approaches.

Description: This paper proposes an integrated stochastic day-ahead scheduling model to dispatch hourly generation and load resources and deploy flexible ramping for managing the variability of renewable energy system. A comprehensive framework for the natural gas transportation network is considered to address the dispatchability of a fleet of fuel-constrained natural gas-fired units. System uncertainties include the day-ahead load and renewable generation forecast errors. Illustrative examples demonstrate that the real-time natural gas delivery can directly impact the hourly dispatch, flexible ramp deployment, and power system operation cost. Meanwhile, the demand side participation can mitigate the dependency of electricity on natural gas by providing a viable option for flexible ramp when the natural gas system is constrained.

Description: If a large disturbance occurs in a power grid, two auxiliary loops for the inertial control of a wind turbine generator have been used: droop loop and rate of change of frequency (ROCOF) loop. Because their gains are fixed, difficulties arise in determining them suitable for all grid and wind conditions. This paper proposes a dynamic droop-based inertial control scheme of a doubly-fed induction generator (DFIG). The scheme aims to improve the frequency nadir (FN) and ensure stable operation of a DFIG. To achieve the first goal, the scheme uses a droop loop, but it dynamically changes its gain based on the ROCOF to release a large amount of kinetic energy during the initial stage of a disturbance. To do this, a shaping function that relates the droop to the ROCOF is used. To achieve the second goal, different shaping functions, which depend on rotor speeds, are used to give a large contribution in high wind conditions and prevent over-deceleration in low wind conditions during inertial control. The performance of the proposed scheme was investigated under various wind conditions using an EMTP-RV simulator. The results indicate that the scheme improves the FN and ensures stable operation of a DFIG.

Description: In this paper, the two-level hierarchical scheme, which consists of wide area centralized and local controls of the power oscillation damper (POD) installed with the doubly-fed induction generator (DFIG) wind turbine and the power system stabilizer (PSS) has been proposed for robust power oscillation damping. In the wide area level, the centralized POD and PSS has received the input signals from synchronized phasor measurement units (PMUs). The geometric measures of controllability and observability have been applied to select the suitable DFIG and synchronous generator (SG) for stabilizing the target oscillation modes, the proper input signals of the centralized POD and PSS, and the location of PMUs. In the local level, the suitable DFIG and SG have been equipped with POD and PSS, respectively. In the parameters optimization of POD and PSS, the practical issues such as damping performance, controller structure, communication latency, and robustness against system uncertainties have been considered. The controller efficiency and resiliency of the proposed controller have been evaluated in comparison with other controllers by eigenvalue analysis and nonlinear simulation for a wide range of operating conditions, line outage contingencies, severe faults, and communication failure.

Description: This paper describes a method for optimal scheduling of hydropower systems for a profit maximizing, price-taking, and risk neutral producer selling energy, and capacity to separate and sequentially cleared markets. The method is based on a combination of stochastic dynamic programming (SDP) and stochastic dual dynamic programming (SDDP), and treats inflow to reservoirs and prices for energy and capacity as stochastic variables. The proposed method is applied in a case study for a Norwegian watercourse, quantifying the expected changes in schedules, and water values when going from an energy-only market to a joint treatment of energy and reserve capacity markets.

Description: In this paper and its companion, the identification of mathematical models describing the behaviour of wave energy devices (WECs) in the ocean is investigated through the use of numerical wave tank (NWT) experiments. This paper deals with the identification tests used to produce the data for the model identification. NWTs, implemented using computational fluid dynamics (CFD), are shown as an effective platform to perform the identification tests. The design of the NWT experiments, to ensure the production of information-rich data for the model identification, is discussed. A case study is presented to illustrate the design and implementation of NWT experiments for the identification of WEC models.

Description: Optimum torque (OT)-based maximum power point tracking (MPPT) is widely used in high power turbines because of its simplicity. Several recent improvements claim better response speeds by augmenting the basic OT algorithm with a suitably scaled inertial torque. However, the underlying dynamic model for all these improved methods is premised on a rigid shaft, which ignores all torsional behavior. This lacuna is addressed in this paper, where a small-signal system description is developed considering a more accurate flexible shaft model. It is shown that the improvements have three possible forms, the third being proposed in this paper. Close-loop stability with each of these is analytically investigated using this accurate small-signal model. It is also proved that one of the reported approaches causes system instability while attempting even moderate improvement over the OT method. The problems in realization of the other two forms are highlighted and a realizable alternative proposed, which requires no additional sensor. Design of the proposed approach is presented in appropriate detail. It is analytically established that the proposed method ensures superior dynamic response. All analytical conclusions are validated by numerical simulations.

Description: Power generation uncertainty is an important characteristic of variable generation (VG) platforms, such as wind and solar power, which brings additional operational costs to the power systems. To manage this uncertainty, responsibilities should be properly allocated to encourage good behaviors of system participants, especially the VG producers. Currently, the imbalance-cost-based mechanism is most commonly used for uncertainty management. Based on this method, we consider a new mechanism in this paper for capturing the uncertainty, which may achieve a better mechanism performance. The basic idea is to allow producers to purchase generation intervals (GIs) for their potential production output. The analysis presented in this paper indicates that producers can be very responsive to this mechanism. With the proper pricing policies, producers can be encouraged to provide additional information on upcoming uncertainties to the system operators. Additionally, three strategies for pricing GIs are included in this paper. Case studies are used to demonstrate the application of the mechanism as well as its effectiveness.

Description: The paper proposes a gossip algorithm-based architecture for managing power flows on radial distribution grids applying decentralized management of demand. Demand flexibility is associated with a cost-like utility function expressing the inconvenience caused by curtailment. Every node of the distribution grid participates in power flow management by sending locally measured and calculated values to its neighboring nodes following a peer-to-peer architecture. The proposed algorithm is based on the application of gossip algorithms to estimate locally critical physical quantities by reaching a global consensus. More specifically, the solution implements gossip algorithms to achieve consensus in aggregated demand and in minimum cost for curtailment of flexible loads/increase of controllable distributed generators.

Description: The objective of this research was to develop and compare various airfoil precomputational parameterization and analysis techniques for aerostructural optimization of wind turbine blades. The airfoils along the blade were added as optimization design variables through precomputational parameterization methods using thickness-to-chord ratios and blended airfoil family factors. The airfoils’ aerodynamic performance was analyzed with three methods of increasing fidelity: a panel method (XFOIL), Navier–Stokes-based computational fluid dynamics (RANS CFD), and wind tunnel data. The optimizations minimized mass over annual energy production ( $m/AEP$ ) and thereby approximated the minimization of cost of energy. The results were compared to the NREL 5-MW reference turbine and a conventional optimization where the airfoils were fixed. Results showed an average $m/AEP$ reduction of 1.7% over conventional optimization methods. The primary benefit in adding the airfoil shape was through an increase in annual energy production (1.6%) with a similar decrease in turbine mass (1.8%). Using the precomputational airfoil parameterization methods provided significant reductions in the cost of energy with relatively minor additional computational cost.

Description: This paper proposes a co-ordinated four-loop switching controller (SC) for the doubly fed induction generator (DFIG) to improve the transient stability of wind power penetrated power systems. A short-term resilience index is introduced, and it reflects the dynamics of both system frequency and load bus voltage. A four-loop SC is driven by the four outputs of a DFIG, namely, the rotor speed deviation, the reactive power output of stator winding, the reactive power transferred through grid-side converter, and the DC-link voltage, respectively. Referring to a state-dependent switching strategy, the four-loop SC switches between a logic-based bang–bang constant funnel controller (LBCFC) and a vector control theory-based conventional controller (CC) in each control loop. The LBCFC is robust to system nonlinearities, uncertainties, and external disturbances. The control signal of the LBCFC is bang–bang with the upper and lower limits of control variables. Simulation studies are undertaken in a modified IEEE 16-generator 68-bus power system, in which four DFIG-based wind farms are penetrated to provide 9.94% power supply. The performance of the four-loop SC is evaluated in aspects of the integral control of the DFIG and the resilience enhancement of the multimachine power system, respectively.

Description: This paper investigates a control strategy for a wind farm with the direct-driven permanent-magnet synchronous generators (PMSG)-based wind turbines and the fixed speed induction generators (FSIG)-based wind turbines under unbalanced grid voltage condition. By controlling the PMSG-based wind farm to inject negative-sequence current for decreasing voltage unbalance factor (VUF) at point of common coupling (PCC), the double grid frequency oscillations in electromagnetic torque, active, and reactive power output from the FSIG-based wind farm can be suppressed. In this paper, the maximum amplitude of the negative-sequence current provided by the PMSG-based wind farm under different average active power output and different VUF conditions is deduced, and the impacts of its phase angle on the VUF mitigation control effect are further studied. The improved control strategy of injecting negative-sequence current from the PMSG-based wind farm by the modified negative-sequence voltage and current double closed-loop control system is then developed. Finally, the correctness of theoretical analysis and the effectiveness of the proposed control strategy are validated by the experimental results.

Description: In this paper and its companion, the identification of mathematical models describing the behaviour of wave energy devices (WECs) in the ocean is investigated through the use of numerical wave tank experiments. When the wave amplitude and the WEC displacement are not negligible with respect to the WEC dimensions, nonlinear hydrodynamic effects may appear, and the accuracy of linear hydrodynamic models is reduced, leading to the necessity of introducing some nonlinearities in the model structure. This paper proposes, for WEC modelling, the use of discrete-time nonlinear autoregressive with exogenous input (NARX) models, as an alternative to continuous-time models. Techniques of model identification are also explained and applied to a case study.

Description: Wind turbine power curves do not consider specific weather conditions, wind shear, turbulence effects of the location where the turbine is going to be installed, or its age. A true power curve is obtained by analyzing data from an installed wind turbine over a year. Here, a model for a true power curve is proposed, considering a normal distribution for each range of wind speed data. Furthermore, a Monte Carlo-based simulation technique is proposed to reproduce data following the normal-based model. The main use of the model is to simulate data to complete lacking real data or to perform long-term assessments. The model was checked with data from two wind turbines at the Sotavento experimental wind farm in the northwest of Spain.

Description: This paper discusses the control of large-scale grid-connected photovoltaic power plant (GCPPP) operating under unbalanced grid voltages. The positive and negative sequences of the grid currents need to be controlled to regulate the power injected into the grid during unbalanced grid voltages. This paper shows that the use of conventional proportional-integral-based controllers compromises stability and dynamic performance of the inverter. The reason is the delays introduced by the filters needed to extract the sequences of the transformed grid currents. Because of such delays, there is a strong restriction on choosing the parameters for the current and voltage controllers, which forces the GCPPP to perform slowly. This can be improved by using resonant controllers instead, which avoid the need for filtering the transformed grid currents. Additionally, a new overcurrent protection is proposed for the GCPPP when it is providing grid voltage support during voltage sags. Simulation and experimental results are presented to evaluate and compare the performance of the GCPPP when operating with the different controllers.

Description: This paper proposes a novel probabilistic transient stability constrained optimal power flow (P-TSCOPF) model to simultaneously consider uncertainties and transient stability for power system preventive control. While detailed wind generator model with rotor flux magnitude and angle control strategy is used to describe the dynamic behaviors of wind generators, uncertain factors with correlations, such as probabilistic load injections, stochastic fault clearing time, and multiple correlated wind generations, are also included to form a representative P-TSCOPF model. A new GSO-PE approach, consisting of an improved group search optimization (GSO) and $2m + 1$ point estimated (PE) method with Cholesky decomposition, is then designed to effectively solve this challenging P-TSCOPF problem. The proposed P-TSCOPF model and GSO-PE solution approach have been thoroughly tested on a modified New England 39-bus system with correlated uncertain wind generations. Comparative results with Monte Carlo (MC) simulations have confirmed the validity of the P-TSCOPF model and demonstrated the effectiveness of GSO-PE method.

Description: Frequency regulation is critical to the successful operation of remote wind–diesel electrical grids. When the grid is in ‘wind–diesel’ mode, frequency regulation is (classically) the sole duty of the diesel electric generator (DEG). An alternative approach is proposed whereby responsibility for frequency regulation is shared by the DEG and a network of autonomous distributed secondary loads (DSLs) consisting of electric thermal storage (ETS) devices. This allows surplus wind to be distributed to residential consumers (as space heat) without the need for a centralized communication network. Numerical modeling of system dynamics with active DSLs is conducted using a SIMULINK wind–diesel hybrid test bed model. The effects of controller gain, installed capacity, switching time and unit coordination timing on frequency and voltage regulation is explored. It is shown that the DSLs can improve frequency regulation in wind–diesel mode while providing storable thermal energy to distributed consumers.

Description: Appropriate wind speed modeling for generating synthetic wind speed data is crucial in microgrid reliability evaluation studies. This paper proposes a 2-D wind speed statistical model based on historical wind speed data. The first dimension of the proposed wind model focuses on the probability distribution on the time duration of different wind speed scales, while the second dimension focuses on the probability distribution of wind speed in each wind speed scale. Unlike traditional wind speed models, the proposed model can simultaneously deal with the probabilistic characteristics of wind speed and wind time duration. This paper also presents a 2-D wind model-based data sampling method, and incorporates it into the microgrid reliability assessment algorithm. Using historical wind speed data in Tianjin, China, it shows that the proposed 2-D wind speed statistical model helps to fully simulate the volatility of wind energy. Furthermore, the proposed wind speed model is applied on the microgrid reliability evaluation study, which shows that the proposed model can be effectively utilized by planners to conduct reliability evaluation for microgrid.

Description: Wind generation is growing fast worldwide. The stochastic variation of large-scale wind generation may impact the power systems in almost every aspect. Probabilistic analysis method is an effective tool to study power systems with random factors. In this paper, a systematic nonlinear analytical probabilistic method is proposed to evaluate the possible effect of random wind power generation on power system small signal stability. A second-order polynomial is proposed to approximate the nonlinear relationship between the wind generation and the damping of a particular dynamic mode, such as the dominant mode. Gaussian mixture model formulates wind uncertainty in a uniform way. Spectral theorem is adopted to reshape the second-order polynomial into a form without cross-product terms. Cholesky decomposition is used to eliminate correlations among outputs of different wind farms. Thereafter the cumulative distribution function (CDF) of the damping ratio with respect to random wind power is consequently constructed. Numerical simulations are carried out in the IEEE standard test system. The proposed method is verified with higher accuracy than the traditional linearized method. Meanwhile, it is much more time-saving in calculation than Monte Carlo simulation.

Description: In this paper, a distributed local control scheme for dc microgrid is proposed along with the basic droop control. It eliminates the limitations of droop control when the distributed generators are geographically distributed, for which, the line resistances cannot be neglected. Effects of line inductance and constant power loading (CPL) are investigated by analyzing the voltage tracking transfer function for single source system. Stability of two sources single load microgrid with proposed controller is investigated. Simulated responses are presented for two sources single load microgrid (for the sake of simplicity) to depict the proper load sharing and voltage improvement capability of the proposed control method with the consideration of line resistances. However, this can be extended to multiple-source multiple-load configuration connected to the dc bus. A comparison of the result is presented to show the better performance of the proposed control scheme as compared to the conventional droop control and hierarchical secondary control. The interconnected operation of the microgrid is also investigated to show the applicability of the proposed control in the interconnected mode. A centralized controller in each area is used to make the tie-line power flow zero at steady state.

Description: There has been an increasing interest in transformerless inverter for grid-tied photovoltaic (PV) system due to low cost, high efficiency, light weight, etc. Therefore, many transformerless topologies have been proposed and verified with real power injection only. Recently, almost every international regulation has imposed that a definite amount of reactive power should be handled by the grid-tied PV inverter. According to the standard VDE-AR-N 4105, grid-tied PV inverter of power rating below 3.68KVA, should attain power factor (PF) from 0.95 leading to 0.95 lagging. In this paper, a new high efficiency transformerless topology is proposed for grid-tied PV system with reactive power control. The new topology structure and detail operation principle with reactive power flow is described. The high frequency common-mode (CM) model and the control of the proposed topology are analyzed. The inherent circuit structure of the proposed topology does not lead itself to the reverse recovery issues even when inject reactive power which allow utilizing MOSFET switches to boost the overall efficiency. The CM voltage is kept constant at mid-point of dc input voltage, results low leakage current. Finally, to validate the proposed topology, a 1 kW laboratory prototype is built and tested. The experimental results show that the proposed topology can inject reactive power into the utility grid without any additional current distortion and leakage current. The maximum efficiency and European efficiency of the proposed topology are measured and found to be 98.54% and 98.29%, respectively.

Description: Wave energy farms may cause voltage flicker on the local grid to which they will be connected due to the strong fluctuations that their output power may present. IEC standard 61400-21 describes methods for estimating the flicker level for different short-circuit ratios as well as for different numbers of devices composing the farm. This method was initially developed for wind farms but is applicable to wave energy farms as well. However, besides the short-circuit ratio and the number of devices composing the farm, the grid impedance angle has also a strong influence on flicker. Despite this, no method exists in the literature for estimating flicker as a function of this variable. This paper presents the results of a study intended to fill this gap by focusing on developing a simplified method for estimating the flicker level induced by a wave energy farm as a function of the grid impedance angle. The results obtained through this method are compared with those obtained from numerical load flow simulations performed with PowerFactory. These simulations were based on experimental power output time series of a wave energy prototype deployed at sea as part of the European CORES project. The voltage profiles thus generated were then processed by means of a flickermeter compliant with IEC standard 61000-4-15.

Description: A novel monotonic strategy following a consistent charging/discharging direction for each individual battery connected in parallel to form a large-scale battery energy storage system (BESS) is proposed in this paper. The BESS is coordinated with a large wind farm to smooth out the intermittent nature of the farm’s output fed to an electricity grid. The strategy is used to optimize the capacity of each battery reducing the system’s capital cost. The strategy also prolongs the battery’s lifetime and consequently minimizes the system’s operating cost. It is shown that the optimal capacity of the battery decreases as their number increases. Moreover, the optimal capacity of the BESS tends to some positive limit as the number of batteries approaches infinity. A rigorous proof of the mathematical theory underlying the proposed strategy and associated optimization are given in the paper. The effectiveness of the strategy is confirmed with data analysis taken from an actual wind farm. The strategy is generic enough to be applicable to other intermittent generation sources such as solar PV farms.