A Comprehensive Review of AI Integration for Fault Detection in Modern Power Systems: Data Processing, Modeling, and Optimization

Youping Liu, Pin Li (), Yang Si and Linrui Ma
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Youping Liu: Qinghai Key Lab of Efficient Utilization of Clean Energy, School of Energy and Electrical Engineering, University of Qinghai, Xining 810016, China
Pin Li: Qinghai Key Lab of Efficient Utilization of Clean Energy, School of Energy and Electrical Engineering, University of Qinghai, Xining 810016, China
Yang Si: Qinghai Key Lab of Efficient Utilization of Clean Energy, School of Energy and Electrical Engineering, University of Qinghai, Xining 810016, China
Linrui Ma: Qinghai Key Lab of Efficient Utilization of Clean Energy, School of Energy and Electrical Engineering, University of Qinghai, Xining 810016, China

Energies, 2025, vol. 18, issue 18, 1-32

Abstract: Driven by the high penetration of renewable energy sources and power electronic devices, modern power systems have become increasingly complex, intensifying the demand for accurate and intelligent fault detection. This paper analyzes a total of 81 references to explore the integrated application of artificial intelligence (AI) technologies across all stages of fault data processing, modeling, and optimization. The application potential of AI in fault data processing is firstly analyzed in terms of its performance in mitigating class imbalance, extracting feature information, handling data noise and classification. Then, the modeling of fault detection is classified into rule-driven, data-driven and hybrid-driven methods to evaluate their applicability in scenarios such as transmission lines and distribution networks. The accuracy of fault detection models is also investigated by studying the hyperparameter optimization (HPO) methods. The results indicate that the utilization of AI-driven imbalance handling enhances model accuracy by a range of 16.2% to 26.2%, while deep learning-based feature extraction techniques sustain accuracy levels exceeding 98.5% under a signal-to-noise ratio (SNR) of 10 dB. With a 99.96% detection accuracy, hybrid-driven models applied in fault detection perform the best. For the optimization of fault detection models, heuristic algorithms provide 6.92–19.375% improvement over the baseline models. The findings suggest that AI-driven methodologies in data processing demonstrate notable noise resilience and other benefits. For modeling fault detection, data-driven and hybrid-driven models are presently extensively employed for detecting short-circuit faults, predicting transformer gas trends, and identifying faults in complex and uncertain scenarios. Conversely, rule-driven models are better suited for scenarios possessing a comprehensive experience library and are utilized with less frequency. In the optimization of fault detection models, heuristic algorithms occupy a pivotal position, whereas hyperparameter optimization incorporating reinforcement learning (RL) is better suited for real-time fault detection. The discoveries presented in this paper facilitate the seamless integration of AI with fault detection in modern power systems, thereby advancing their intelligent evolution.

Keywords: data processing; fault detection model; hyperparameter optimization; artificial intelligence; 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: 2025
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