Partial discharge ultrasonic signals pattern recognition in transformer using BSO-SVM based on microfiber coupler sensor. (30th September 2022)

• Record Type: Journal Article
• Authors: Zhou, Yang; Liu, Yiying; Wang, Na; Han, Xutao; Li, Junhao
• Is Part Of: Measurement. Volume 201(2022)
• Publication Date: 2022-09-30
• Subjects: Partial discharge -- Microfiber coupler sensor -- Pattern recognition -- BSO-SVM; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2022.111737
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: Partial discharge ultrasonic signals pattern recognition in transformer. The application of microfiber coupler sensor (MFCS) in the field of transformer partial discharge fault diagnosis. Optimized support vector machine (SVM) algorithm. BSO-SVM classification algorithm. Abstract: Detection and pattern recognition of different types of partial discharge (PD) signals in transformer are of great significance for the safe and stable operation of transformers. This paper demonstrates the application of microfiber coupler sensor (MFCS) to the pattern recognition of PD ultrasonic signals with an optimized support vector machine (BSO-SVM) algorithm. First, a PD ultrasonic detection system based on MFCS is built, compared with the traditional piezoelectric sensor, the MFCS can be directly implanted in the transformer oil to detect the PD signals, avoiding the attenuation of ultrasonic signals as they propagate to the transformer outer wall. Then, four kinds of PD ultrasonic signals are collected by the PD detection system, and feature extraction is carried out for the time domain and frequency domain characteristics of PD ultrasonic signals. Finally, a BSO-SVM classification algorithm is proposed, which uses the beetle antennae search (BAS) and the particle swarm optimization (PSO) algorithm to jointly optimize the support vector machine (SVM) algorithm. The BSO-SVM classifier is used to classify four kinds of PD signals, the classification accuracy is 93%, and the number of convergence is 5. In addition, the BSO-SVM algorithm is compared with the GA-SVM and PSO-SVM algorithms. The results show that the BSO-SVM algorithm has higher recognition accuracy and faster convergence speed. The research results show that it is feasible to use MFCS combined with BSO-SVM to identify PD patterns of transformers, and it has certain significance for transformer fault diagnosis.