Partial discharge ultrasonic signals pattern recognition in transformer using BSO-SVM based on microfiber coupler sensor

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 mi-crofiber 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 classi-fication 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.

Automated summary

This paper demonstrates the application of microfiber coupler sensor (MFCS) and optimized support vector machine (BSO-SVM) algorithm in the pattern recognition of transformer partial discharge (PD) signals. The results show that the combination of MFCS and BSO-SVM achieves high classification accuracy and fast convergence speed, which is of great significance for transformer fault diagnosis.