Research on fault identification of high-voltage circuit breakers with characteristics of voiceprint information

Abstract

High voltage circuit breakers are one of the core equipment in power system operation, and the voiceprint signals generated during operation contain extremely rich information. This paper proposes a fault identification method for high voltage circuit breakers based on voiceprint information data. Firstly, based on the developed voiceprint information data acquisition device, the voiceprint information of a certain high voltage circuit breaker is obtained; Secondly, an improved S-transform is proposed in the article, which generates an amplitude matrix based on the S-transform of voiceprint information; Then, through the matrix Singular value decomposition method, the fault feature quantity of voiceprint information is extracted from the time–frequency angle, and the diagnosis system of the support vector machine model is established, and the system is trained to realize the fault identification of the high-voltage circuit breaker; Finally, through experimental simulation calculations, it was shown that the accuracy of the proposed fault identification method in different operating conditions reached 92.6%, verifying the good accuracy and robustness of the proposed method and equipment.

Research on circuit breaker operating mechanism feature extraction method combining ICEEMDAN-MRSVD denoising and VMD-PSE

“…The traditional neural network classification method requires a large number of training samples, the larger the number of samples, the more complete the training model, and the higher the final recognition accuracy. SVM [26] is a recognition method based on small sample training, with the advantages of requiring fewer samples and higher classification accuracy, which is more suitable for CB fault detection and classification. In the SVM training process, the RBF is selected as the kernel function, and the penalty function parameter c and radial basis kernel function parameter g are selected by the cross-validation method to improve the recognition efficiency and recognition accuracy, and after optimization, c = 9.766 and g = 0.303 are selected.…”