ALBERT

Description: This paper assessed the impact of soil and water conservation practices on farm productivity and risk exposure using data from 1204 plots in the semiarid tropics of India. A probit model was used to assess the determinants of adoption of soil bunds. We employed a moment-based approach for estimating crop revenue, its variability and downside risk exposure, i.e., crop failure. Furthermore, we also used a doubly robust method for assessing the impact of soil bunds on crop revenue, its variability and downside risk. Matching and propensity-based methods were also used to check robustness. The results show that training, access to credit and extension services are key determinants of adoption of soil bunds. Furthermore, the results also suggest that soil bunds not only improve the crop revenue but also reduce its variability. Most interestingly, we show that soil bunds also reduce the chances of downside risk, i.e., crop failure. Therefore, in view of increasing climate change and variability in the semiarid tropics, it can be suggested that soil bunds could be an important adaptation strategy for improving productivity and reducing risk exposure. This paper supports the investment in soil and water conservation technologies for sustaining the livelihood of resource-poor farmers of ecologically fragile regions such as the semiarid tropics.

Description: Penetration level of solar photovoltaic (PV) energy in the utility network is steadily increasing. This changes the fault level and causes protection problems. Furthermore, multi-tapped structure of distribution network deployed to integrate solar PV energy to the grid and supplying loads at the same time also raised the protection challenges. Hence, this manuscript is aimed at introducing an algorithm to identify and classify the faults incident on the network of utilities where penetration level of the solar PV energy is high. This fault recognition algorithm is implemented in four steps: (1) calculation of Stockwell transform-based fault index (STFI) (2) calculation of Wigner distribution function-based fault index (WDFI) (3) calculation of combined fault index (CFI) by multiplying STFI and WDFI (4) calculation of index for ground fault (IGF) used to recognize the involvement of ground in a fault event. The STFI has the merits that its performance is least affected by the noise associated with the current signals and it is effective in identification of the waveform distortions. The WDFI employs energy density of the current signals for estimation of the faults and takes care of the current magnitude. Hence, CFI has the merit that it considers the current magnitude as well as waveform distortion for recognition of the faults. The classification of faults is achieved using the number of faulty phases. An index for ground fault (IGF) based on currents of zero sequence is proposed to classify the two phase faults with and without the ground engagement. Investigated faults include phase to ground, two phases fault without involving ground, two phases fault involving ground and three phase fault. Fault recognition algorithm is tested for fault recognition with the presence of noise, various angles of fault incidence, different impedances involved during faulty event, hybrid lines consisting of overhead line (OHL) and underground cable (UGC) sections, and location of faults on all nodes of the test grid. Fault recognition algorithm is also tested to discriminate the transients due to switching operations of feeders, loads and capacitor banks from the faulty transients. Performance of the fault recognition algorithm is compared with the algorithms based on discrete wavelet transform (DWT), Stockwell transform (ST) and hybrid combination of alienation coefficient and Wigner distribution function (WDF). Effectiveness of the fault recognition algorithm is established using a detailed study on the IEEE-13 nodes test feeder modified to incorporate solar PV plant of capacity 1 MW in MATLAB/Simulink. Algorithm is also validated on practical utility grid of Rajasthan State of India.

Description: Despite the tremendous efforts exhibited by various utilities around the world during the past few years, there are still exceedingly many remote regions unreached by the electrical grid. For those regions, the enormous available potential of renewable energy resources is believed to be useful for the development of a stand-alone power supply system. This paper presents the modeling of a stand-alone hybrid system for the remote area of Ethiopia. A comparison of the economic performance of various scenarios of a stand-alone photovoltaic (PV)-wind hybrid system, with battery storage and diesel as a backup for electrifying remote rural areas, is presented. Therefore, a practical example, Kutur village of Awlio kebele of the Axum district, Ethiopia (which is 30 km away from the closest national grid) is considered for this research. Two electric load scenarios are estimated by considering the set of incandescent and efficient lamps for lighting for the 120 existing households. The above-mentioned solar radiation and wind speed are then used as an input to simulate the hybrid set-up for the high and low load estimation using HOMER software. The simulation result shows that the net present costs (NPC) corresponding to the high and low load scenarios is $262,470 and $180,731, respectively. Besides, an essential load forecasting is performed to see the effect of the increase in electric demand of the community on the required investment to install a stand-alone hybrid set-up. The NPC after load forecasting is found to be more than three folds of the NPC required for the reference year. In both cases, the simulation results indicate that using a stand-alone PV-wind hybrid system with battery storage and a diesel generator as a backup for electrifying Kutur village is cost-effective and comparable against the cost required for electrifying the village by extending the grid.

Description: Penetration level of renewable energy (RE) in the utility grid is continuously increasing to minimize the environmental concerns, risk of energy security, and depletion of fossil fuels. The uncertain nature and availability of RE power for a short duration have created problems related to the protection, grid security, power reliability, and power quality. Further, integration of RE sources near the load centers has also pronounced the protection issues, such as false tripping, delayed tripping, etc. Hence, this paper introduces a hybrid grid protection scheme (HGPS) for the protection of the grid with RE integration. This combines the merits of the Stockwell Transform, Hilbert Transform, and Alienation Coefficient to improve performance of the protection scheme. The Stockwell Transform-based Median and Summation Index (SMSI) utilizing current signals, Hilbert Transform-based derivative index (HDI) utilizing voltage signals, and Alienation Coefficient index (ACI) utilizing voltage signals were used to compute a proposed Stockwell Transform-, Hilbert Transform-, and Alienation-based fault index (SAHFI). This SAHFI was used to recognize the fault conditions. The fault conditions were categorized using the number of faulty phases and the proposed Stockwell Transform and Hilbert Transform-based ground fault index (SHGFI) utilizing zero sequence currents. The fault conditions, such as phase and ground (PGF), any two phases (TPF), any two phases and ground (TPGF), all three phases (ATPF), and all three phases and ground (ATPGF), were recognized effectively, using the proposed SAHFI. The proposed method has the following merits: performance is least affected by the noise, it is effective in recognizing fault conditions in minimum time, and it is also effective in recognizing the fault conditions in different scenarios of the grid. Performance of the proposed approach was found to be superior compared to the discrete wavelet transform (DWT)-based method reported in the literature. The study was performed using the hybrid grid test system realized by integrating wind and solar photovoltaic (PV) plants to the IEEE-13 nodes network in MATLAB software.

Description: Water contamination is often reported in agriculturally intensive areas such as the Indo-Gangetic Plain (IGP) in south-eastern Asia. We evaluated the impact of the organic and conventional farming of basmati rice on water quality during the rainy season (July to October) of 2011 and 2016 at Kaithal, Haryana, India. The study area comprised seven organic and seven conventional fields where organic farming has been practiced for more than two decades. Water quality parameters used for drinking (nitrate, NO3; total dissolved solids (TDS); electrical conductivity (EC) pH) and irrigation (sodium adsorption ratio (SAR) and residual sodium carbonate (RSC)) purposes were below permissible limits for all samples collected from organic fields and those from conventional fields over the long-term (~15 and ~20 years). Importantly, the magnitude of water NO3 contamination in conventional fields was approximately double that of organic fields, which is quite alarming and needs attention in future for farming practices in the IGP in south-eastern Asia.

Description: : The main aim of this study is to assess groundwater potential of the DakNong province, Vietnam, using an advanced ensemble machine learning model (RABANN) that integrates Artificial Neural Networks (ANN) with RealAdaBoost (RAB) ensemble technique. For this study, twelve conditioning factors and wells yield data was used to create the training and testing datasets for the development and validation of the ensemble RABANN model. Area Under the Receiver Operating Characteristic (ROC) curve (AUC) and several statistical performance measures were used to validate and compare performance of the ensemble RABANN model with the single ANN model. Results of the model studies showed that both models performed well in the training phase of assessing groundwater potential (AUC ≥ 0.7), whereas the ensemble model (AUC = 0.776) outperformed the single ANN model (AUC = 0.699) in the validation phase. This demonstrated that the RAB ensemble technique was successful in improving the performance of the single ANN model. By making minor adjustment in the input data, the ensemble developed model can be adapted for groundwater potential mapping of other regions and countries toward more efficient water resource management. The present study would be helpful in improving the groundwater condition of the area thus in solving water borne disease related health problem of the population.

Description: Groundwater is one of the most important sources of fresh water all over the world, especially in those countries where rainfall is erratic, such as Vietnam. Nowadays, machine learning (ML) models are being used for the assessment of groundwater potential of the region. Credal decision trees (CDT) is one of the ML models which has been used in such studies. In the present study, the performance of the CDT has been improved using various ensemble frameworks such as Bagging, Dagging, Decorate, Multiboost, and Random SubSpace. Based on these methods, five hybrid models, namely BCDT, Dagging-CDT, Decorate-CDT, MBCDT, and RSSCDT, were developed and applied for groundwater potential mapping of DakLak province of Vietnam. Data of 227 groundwater wells of the study area were utilized for the construction and validation of the models. Twelve groundwater potential conditioning factors, namely rainfall, slope, elevation, river density, Sediment Transport Index (STI), curvature, flow direction, aspect, soil, land use, Topographic Wetness Index (TWI), and geology, were considered for the model studies. Various statistical measures, including area under receiver operating characteristic (AUC) curve, were applied to validate and compare the performance of the models. The results show that performance of the hybrid CDT ensemble models MBCDT (AUC = 0.770), BCDT (AUC = 0.731), Dagging-CDT (AUC = 0.763), Decorate-CDT (AUC = 0.750), and RSSCDT (AUC = 0.766) improved significantly in comparison to the single CDT (AUC = 0.722) model. Therefore, these developed hybrid models can be applied for better ground water potential mapping and groundwater resources management of the study area as well as other regions of the world.

Description: A distribution network is one of the main parts of a power system that distributes power to customers. While there are various types of power distribution networks, a recently introduced novel structure of an aromatic network could begin a new era in the distribution levels of power systems and designs of microgrids or smart grids. In order to minimize blackout periods during natural disasters and provide sustainable energy, improve energy efficiency and maintain stability of a distribution network, it is essential to configure/reconfigure the network topology based on its geographical location and power demand, and also important to realize its self-healing function. In this paper, a strategy for reconfiguring aromatic networks based on structures of natural aromatic molecules is explained. Various network structures are designed, and simulations have been conducted to justify the performance of each configuration. It is found that an aromatic network does not need to be fixed in a specific configuration (i.e., a DDT structure), which provides flexibility in designing networks and demonstrates that the successful use of such structures will be a perfect solution for both distribution networks and microgrid systems in providing sustainable energy to the end users.

Description: Electric vehicles (EVs) can be considered an alternative technology to reduce greenhouse gas emissions in the transportation sector. However, numerous barriers need to be overcome in this regard. This study is aimed at presenting the framework for the identification and analysis of barriers against the use of EVs. Then, the framework was applied to identify the challenges and rank them in order of importance against the diffusion of EVs in Nepal. Seventeen barriers were identified from previous studies, reports, policy documents, and interactions with experts. The identified barriers were classified into five categories: technical, policy, economic, infrastructure, and social. Then, a comparative survey was performed to obtain experts’ opinions on the identified barriers and the analytical hierarchical process was used to analyze and rank them. The results revealed that infrastructure, policy, economic, and technical barriers pose more pressing concerns than social barriers. The lack of charging stations, relatively higher purchase price of EVs compared to internal combustion vehicles, and poor long-term planning and goal setting on the part of the government were ranked as the top three barriers against EV uptake in Nepal. This framework can be applied to replicate the study in other countries, taking their inherent relevant factors into account.

Description: The integration of smart grid technologies in interconnected power system networks presents multiple challenges for the power industry and the scientific community. To address these challenges, researchers are creating new methods for the validation of: control, interoperability, reliability of Internet of Things systems, distributed energy resources, modern power equipment for applications covering power system stability, operation, control, and cybersecurity. Novel methods for laboratory testing of electrical power systems incorporate novel simulation techniques spanning real-time simulation, Power Hardware-in-the-Loop, Controller Hardware-in-the-Loop, Power System-in-the-Loop, and co-simulation technologies. These methods directly support the acceleration of electrical systems and power electronics component research by validating technological solutions in high-fidelity environments. In this paper, members of the Survey of Smart Grid International Research Facility Network task on Advanced Laboratory Testing Methods present a review of methods, test procedures, studies, and experiences employing advanced laboratory techniques for validation of range of research and development prototypes and novel power system solutions.