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• W4207033411 abstract "This chapter presents an intelligent fault diagnosis (IFD) scheme for a four-node test distribution feeder combining advanced signal processing techniques and machine learning tools (MLTs). It starts with modeling the mentioned feeder and faults by varying the prefault loading conditions and fault information (resistance and inception angle). Then, it extracts useful features from the recorded current signals employing discrete wavelet transform (DWT) and stockwell transform (ST). Finally, it fetches the extracted features into three different MLT namely, the artificial neural networks (ANNs), support vector machines (SVMs), and extreme learning machines (ELMs) for the development of fault detection, classification, location schemes. Moreover, the MLT control parameters are also tuned using metaheuristic optimization algorithms for better generalization performance. Obtained results confirm the efficacy of the IFD schemes and their independence in prefault loading conditions, fault information, and the presence of measurement noises." @default.
• W4207033411 created "2022-01-26" @default.
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• W4207033411 date "2022-01-01" @default.
• W4207033411 modified "2023-09-26" @default.
• W4207033411 title "Intelligent fault diagnosis technique for distribution grid" @default.
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• W4207033411 doi "https://doi.org/10.1016/b978-0-323-88429-7.00005-9" @default.
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