A Review on the Application of Artificial Intelligence in Anomaly Analysis Detection and Fault Location in Grid Indicator Calculation Data

Shiming Sun, Yuanhe Tang, Tong Tai, Xueyun Wei and Wei Fang ()
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Shiming Sun: Nari Group Corporation (State Grid Electric Power Research Institute), Nanjing 211106, China
Yuanhe Tang: Nari Group Corporation (State Grid Electric Power Research Institute), Nanjing 211106, China
Tong Tai: Nari Group Corporation (State Grid Electric Power Research Institute), Nanjing 211106, China
Xueyun Wei: State Grid Jiangsu Electric Power Co., Ltd., Power Dispatching and Control Center, Nanjing 210024, China
Wei Fang: School of Computer Science, Nanjing University of Information Science & Technology, Nanjing 210044, China

Energies, 2024, vol. 17, issue 15, 1-15

Abstract: With the rapid development of artificial intelligence (AI), AI has been widely applied in anomaly analysis detection and fault location in power grid data and has made significant research progress. Through looking back on traditional methods and deep learning methods in anomaly analysis detection and fault location of power grid data, we aim to provide readers with a comprehensive understanding of the existing knowledge and research advancements in this field. Firstly, we introduce the importance of anomaly analysis detection and fault location in power grid data for the safety and stability of power system operations and review traditional methods for anomaly analysis detection and fault location in power grid data, analyzing their advantages and disadvantages. Next, the paper briefly introduces the concepts of commonly used deep learning models in this field and explores, in depth, the application of deep learning methods in anomaly analysis detection and fault location of power grid data, summarizes the current research progress, and highlights the advantages of deep learning over traditional methods. Finally, we summarize the current issues and challenges faced by deep learning in this field and provide an outlook on future research direction.

Keywords: artificial intelligence; power grid data; anomaly analysis detection; fault location; deep learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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