Artificial Intelligence-Based Fault Detection in Electrical Power Systems
DOI:
https://doi.org/10.66238/jaeee34Keywords:
Artificial intelligence, fault detection, power systems, machine learning, smart gridsAbstract
Electrical power systems are increasingly complex because of growing electricity demand, renewable energy integration, distributed generation, and the expansion of digital monitoring technologies. These developments create new challenges for detecting and diagnosing faults quickly and accurately. Artificial intelligence (AI) offers methods for identifying patterns in electrical measurements that may be difficult to distinguish using fixed thresholds alone. This article reviews the application of AI to fault detection, classification, and location in power systems. It discusses commonly used data sources, signal processing techniques, machine learning algorithms, and deep learning models. A practical framework is presented for collecting electrical measurements, preparing datasets, training models, and integrating diagnostic outputs into power system monitoring. The article also considers challenges involving limited fault data, imbalanced classes, changing network conditions, cybersecurity, interpretability, and real-time operation. The review finds that AI can support faster and more informative fault diagnosis, but its effectiveness depends on representative data, appropriate testing, and careful integration with established protection systems. Future research should focus on robust and explainable models, realistic field validation, and improved cooperation between AI-based monitoring and conventional protection devices.
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Copyright (c) 2026 Elena Marquez
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