Fault detection in switchgear based on RFID sensors and deep learning
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(Electric Power Research Institute, State Grid Jiangsu Electric Power Co., Ltd., Nanjing 211103, China)

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TM591

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    Abstract:

    In order to improve the accuracy of switchgear fault detection, this paper proposes a fault detection algorithm for switchgear based on RFID sensors and deep learning. Firstly, RFID sensing tags are designed to collect the current signals and temperature of the switchgear. Secondly, the collected signals are subjected to deep-level feature extraction through a deep belief network (DBN), and sparse coding (SC) is integrated into the DBN to improve its detection accuracy. Finally, in order to improve the detection speed, an extreme learning machine (ELM) is used to classify and recognize the signals extracted from the features. The experimental results show that compared to other algorithms, the sparse DBN-ELM (SDBN–ELM) fault detection model proposed in the paper offers higher detection accuracy, faster recognition speed, and an accuracy rate of 99.63%.

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王 真,刘子全,路永玲,李玉杰.基于RFID传感器和深度学习的开关柜故障诊断研究[J].电力科学与技术学报英文版,2025,40(2):179-185. WANG Zhen, LIU Ziquan, LU Yongling, LI Yujie. Fault detection in switchgear based on RFID sensors and deep learning[J]. Journal of Electric Power Science and Technology,2025,40(2):179-185.

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  • Online: June 06,2025
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