{"id":9149,"date":"2026-07-10T00:04:51","date_gmt":"2026-07-09T16:04:51","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=9149"},"modified":"2026-07-10T00:59:42","modified_gmt":"2026-07-09T16:59:42","slug":"jase-202610-33-033","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202610-33-033","title":{"rendered":"An Intelligent Fault Prediction and Self-Healing Framework for Electrical Automation Systems Using Deep Reinforcement Learning"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 33<\/a><\/p>\n<\/div>\n\n\n\n

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Songhua Cao<\/i><\/a><\/p>\n\n\n\n

Shijiazhuang College of Applied Technology, Department of electrical and electronic engineering, Shijiazhuang, 050800, China<\/p>\n<\/div>\n\n\n\n

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Received: March 29, 2026
Accepted: May 27, 2026
Publication Date: July 10, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

DRL Framework for Fault Prediction and Self-Healing in Electrical Systems<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202610_33.033<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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Modern smart grids have electrical automation systems that need smart mechanisms to predict faults early and recover fast to maintain the system stability and reliability. Nonetheless, the traditional fault detection methods have a weakness in that they cannot effectively represent the temporal relationships in power system signals, slow recovery processes, and excessive system downtime in response to disturbances. To overcome these difficulties, the present paper proposes an intelligent fault prediction and self-healing system, which combines a Temporal Convolutional Network (TCN) and Deep Reinforcement Learning (DRL). The model then predicts possible faults through the TCN model that is trained on the IEEE-39 bus power system data with the time-series data on voltage, frequency and power flow parameters under different operating conditions. The DRL agent subsequently becomes aware of the best control measures to curb flaws and reinstate stability in the system. The suggested model is able to withstand the shortcomings of conventional approaches due to its ability to integrate precise learning of temporal patterns with the adaptability of decision-making. The experimental findings indicate that the proposed model, which gives 98.2% accuracy, 97.5% precision, 98.8% recall, and 98.1%
F1-score and has an ROC-AUC of 0.985, indicates high accuracy and precision. Also, the system demonstrates an average recovery time of 120 ms and a voltage deviation of 0.02 p.u., which is much higher than the grid resilience and operational reliability. Overall, the proposed framework provides an effective and intelligent solution for predictive fault management and automated self-healing in modern electrical automation systems.<\/p>\n\n\n\n

Keywords: Fault Prediction, Self-Healing Power Systems, TCN, DRL, Smart Grid Automation, Electrical Automation Systems<\/em><\/p>\n\n\n\n

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