Composite Insulator Defect Identification Method Based on Acoustic–Electric Feature Fusion and MMSAE Network

Bizhen Zhang, Shengwen Shu (), Cheng Chen, Xiaojie Wang, Jun Xu and Chaoying Fang
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Bizhen Zhang: School of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China
Shengwen Shu: School of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China
Cheng Chen: Fuzhou Power Supply Company, State Grid Fujian Electric Power Co., Ltd., Fuzhou 350009, China
Xiaojie Wang: Electric Power Research Institute, State Grid Fujian Electric Power Co., Ltd., Fuzhou 350007, China
Jun Xu: Electric Power Research Institute, State Grid Fujian Electric Power Co., Ltd., Fuzhou 350007, China
Chaoying Fang: Electric Power Research Institute, State Grid Fujian Electric Power Co., Ltd., Fuzhou 350007, China

Energies, 2023, vol. 16, issue 13, 1-21

Abstract: Aiming to solve the partial discharge problem caused by defects in composite insulators, most existing live detection methods are limited by the subjectivity of human judgment, the difficulty of effective quantification, and the use of a single detection method. Therefore, a composite insulator defect diagnosis model based on acoustic–electric feature fusion and a multi-scale perception multi-input of stacked auto-encoder (MMSAE) network is proposed in this paper. Initially, during the withstanding voltage experiment, the electromagnetic wave spectrometer and ultrasonic detector were used to collect and process the data of six types of composite insulator samples with artificial defects. The electromagnetic wave spectrum, ultrasonic power spectral density, and n - S map were then obtained. Then, the network architecture of MMSAE was built by integrating a stacked auto-encoder and multi-scale perception module; the feature extraction and fusion methods of the electromagnetic wave spectrum and ultrasonic signal were investigated. The proposed method was used to diagnose test samples, and the diagnostic results were compared to those obtained using a single input source and the artificial neural network (ANN) method. The results demonstrate that the detection accuracy of acoustic–electric feature fusion is greater than that of a single feature; the accuracy of the proposed method is 99.17%, which is significantly higher than the accuracy of the conventional ANN method. Finally, composite insulator defect diagnosis software based on PYQT5 and Keras was developed. Ten 500 kV aging composite insulators were used to validate the effectiveness of the proposed method and design software.

Keywords: composite insulator; defect identification; deep learning; feature fusion; electromagnetic wave spectrum; ultrasonic (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: 2023
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