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- W4383269491 endingPage "14549" @default.
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- W4383269491 abstract "<abstract><p>In modern power systems, efficient ground fault line selection is crucial for maintaining stability and reliability within distribution networks, especially given the increasing demand for energy and integration of renewable energy sources. This systematic review aims to examine various artificial intelligence (AI) techniques employed in ground fault line selection, encompassing artificial neural networks, support vector machines, decision trees, fuzzy logic, genetic algorithms, and other emerging methods. This review separately discusses the application, strengths, limitations, and successful case studies of each technique, providing valuable insights for researchers and professionals in the field. Furthermore, this review investigates challenges faced by current AI approaches, such as data collection, algorithm performance, and real-time requirements. Lastly, the review highlights future trends and potential avenues for further research in the field, focusing on the promising potential of deep learning, big data analytics, and edge computing to further improve ground fault line selection in distribution networks, ultimately enhancing their overall efficiency, resilience, and adaptability to evolving demands.</p></abstract>" @default.
- W4383269491 created "2023-07-06" @default.
- W4383269491 creator A5000155541 @default.
- W4383269491 creator A5006993614 @default.
- W4383269491 creator A5019391982 @default.
- W4383269491 creator A5024302139 @default.
- W4383269491 creator A5025689891 @default.
- W4383269491 creator A5032552303 @default.
- W4383269491 creator A5059169221 @default.
- W4383269491 creator A5074554020 @default.
- W4383269491 creator A5089966579 @default.
- W4383269491 date "2023-01-01" @default.
- W4383269491 modified "2023-09-30" @default.
- W4383269491 title "Artificial intelligence techniques for ground fault line selection in power systems: State-of-the-art and research challenges" @default.
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