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  • 1
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    High Voltage Engineering and Applications (2021)
    MDPI - Multidisciplinary Digital Publishing Institute
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    Publication Date: 2024-04-11
    Description: This book is a collection of recent publications from researchers all over the globe in the broad area of high-voltage engineering. The presented research papers cover both experimental and simulation studies, with a focus on topics related to insulation monitoring using state-of-the-art sensors and advanced machine learning algorithms. Special attention was given in the Special Issue to partial discharge monitoring as one of the most important techniques in insulation condition assessment. Moreover, this Special Issue contains several articles which focus on different modeling techniques that help researchers to better evaluate the condition of insulation systems. Different power system assets are addressed in this book, including transformers, outdoor insulators, underground cables, and gas-insulated substations.
    Keywords: TA1-2040 ; T1-995 ; artificial neural network ; simulation ; high-frequency ; artificial flashover tests ; wide bandgap power modules ; tracking ; electrical field strength ; fast-rise square wave voltages ; FDTD simulation ; cable joint ; corona discharge ; feature selection ; post insulator ; earthing systems ; wind speed ; surface discharge ; oil/paper insulation ; oil-paper insulation ; high-magnitude currents and impulse polarity ; UFVM ; Tettex 9520 ; electrical tree ; flashover characteristics ; composite insulator ; partial discharge ; numerical modeling ; saline mechanism ; thermal parameters ; space charge density ; seasonal ; ion flow field ; denoising ; DDX 9121b ; temperature ; transformer asset management ; cavity discharge ; space/interface charge ; insulation health index ; heat transfer model ; leakage current ; machine learning ; partial discharges (PD) ; RF signal ; flashover ; high impulse conditions ; grounding electrodes ; generalized finite difference time domain ; curve fitting ; grounding ; plasma discharge ; outdoor insulators ; flashover dynamic model ; wavelet transform ; degradation ; thermal properties ; bipolar charge transport model ; UHF sensor ; cable ; random walk theory ; tracking test setup ; GIL ; pressure ; modelling ; non-uniform pollution between windward and leeward sides ; calibrator ; secondary arc ; polymeric insulation ; optical-UHF integrated detection ; shoreline ; dry band arcing ; photoelectric fusion pattern ; DDX 8003 ; XLPE ; silicone gel ; partial discharge modeling ; electric field analysis ; NSCT ; electrode’s geometry ; gas ; Comsol Multiphysics ; fast-impulses ; laying modes ; ageing ; cable ampacity ; residual resistance formulation ; finite element analysis ; thermal effect ; hydrophobicity ; soil resistivity ; charge simulation method ; short-circuit discharge ; dry band formation ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology
    Language: English
    Format: application/octet-stream
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  • 2
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    Two-Layer Ensemble-Based Soft Voting Classifier for Transformer Oil Interfacial Tension Prediction (2020)
    Molecular Diversity Preservation International
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    Publication Date: 2020-04-05
    Description: This paper uses a two-layered soft voting-based ensemble model to predict the interfacial tension (IFT), as one of the transformer oil test parameters. The input feature vector is composed of acidity, water content, dissipation factor, color and breakdown voltage. To test the generalization of the model, the training data was obtained from one utility company and the testing data was obtained from another utility. The model results in an optimal accuracy of 0.87 and a F1-score of 0.89. Detailed studies were also carried out to find the conditions under which the model renders optimal results.
    Electronic ISSN: 1996-1073
    Topics: Energy, Environment Protection, Nuclear Power Engineering
    Published by Molecular Diversity Preservation International
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  • 3
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    Denoising of Radio Frequency Partial Discharge Signals Using Artificial Neural Network (2019)
    Molecular Diversity Preservation International
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    Publication Date: 2019-09-10
    Description: One of the most promising techniques for condition monitoring of high voltage equipment insulation is partial discharge (PD) measurement using radio frequency (RF) antenna. Nevertheless, the accuracy of monitoring, classification, localization, or lifetime estimation could be negatively affected due to the interferences and noises measured simultaneously and contaminate the RF signals. Therefore, to achieve high accuracy of PD assessment, exploiting the denoising algorithms is inevitable. Hence, this paper seeks to introduce a new technique to suppress white noise, the most prevalent type of noise, especially for RF signals. In the proposed method, the ability of artificial neural network (ANN) in curve fitting is applied to denoising of different types of measured RF signals emitted from PD sources including ‘crack’, ‘internal void’, in the insulator discs and ‘sharp points’ from external hardware. The processes of denoising for named signals with the proposed method are carried out, and the obtained results are compared with the outputs of a wavelet transform-based method named energy conversation-based thresholding. In all tested signals, the proposed technique showed superior denoising capability.
    Electronic ISSN: 1996-1073
    Topics: Energy, Environment Protection, Nuclear Power Engineering
    Published by Molecular Diversity Preservation International
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  • 4
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    Application of Machine Learning in Transformer Health Index Prediction (2019)
    Molecular Diversity Preservation International
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    Publication Date: 2019-07-14
    Description: The presented paper aims to establish a strong basis for utilizing machine learning (ML) towards the prediction of the overall insulation health condition of medium voltage distribution transformers based on their oil test results. To validate the presented approach, the ML algorithms were tested on two databases of more than 1000 medium voltage transformer oil samples of ratings in the order of tens of MVA. The oil test results were acquired from in-service transformers (during oil sampling time) of two different utility companies in the gulf region. The illustrated procedure aimed to mimic a realistic scenario of how the utility would benefit from the use of different ML tools towards understanding the insulation health index of their transformers. This objective was achieved using two procedural steps. In the first step, three different data training and testing scenarios were used with several pattern recognition tools for classifying the transformer health condition based on the full set of input test features. In the second step, the same pattern recognition tools were used along with the three training/testing scenarios for a reduced number of test features. Also, a previously developed reduced model was the basis to reduce the needed number of tests for transformer health index calculations. It was found that reducing the number of tests did not influence the accuracy of the ML prediction models, which is considered as a significant advantage in terms of transformer asset management (TAM) cost reduction.
    Electronic ISSN: 1996-1073
    Topics: Energy, Environment Protection, Nuclear Power Engineering
    Published by Molecular Diversity Preservation International
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  • 5
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    Smart Home Renewable Energy Management System (2011)
    Elsevier
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    Publication Date: 2011-01-01
    Electronic ISSN: 1876-6102
    Topics: Energy, Environment Protection, Nuclear Power Engineering
    Published by Elsevier
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  • 6
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    Development of Hankel Singular-Hypergraph Feature Extraction Technique for Acoustic Partial Discharge Pattern Classification (2021)
    Molecular Diversity Preservation International
    Details
    Publication Date: 2021-03-12
    Description: Different types of classifiers for acoustic partial discharge (PD) pattern classification have been widely discussed in the literature. The classifier performance mainly depends on the measurement conditions (location and type of the PD, acoustic sensor position and frequency response) as well as extracted features. Recent research posits that features extracted by singular value decomposition (SVD) can exhibit the natural characteristics and energy contained in the signal. Though the technique by itself is not novel, in this paper, SVD is employed for PD classification in a revised way starting from data arrangement in Hankel form, to embedding the hypergraph-based features and finally to extracting the required set of optimal features. The algorithm is tested for various measurement conditions that include the influences of various PD locations and oil temperatures. The robustness of the algorithm is also tested using noisy PD signals. Experimental results show the proposed feature extraction method supremacy.
    Electronic ISSN: 1996-1073
    Topics: Energy, Environment Protection, Nuclear Power Engineering
    Published by Molecular Diversity Preservation International
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