Wang Sihao, Zhou Yongrong, Ma Zhaoxing
State Grid Electric Power Research Institute Co., Ltd., Nanjing, 211106, China.
School of Information and Control Engineering, Qingdao University of Technology, Qingdao, 266520, China.
Sci Rep. 2024 Apr 23;14(1):9340. doi: 10.1038/s41598-024-59999-0.
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.
高压断路器是电力系统运行中的核心设备之一，其运行过程中产生的声纹信号蕴含着极为丰富的信息。本文提出了一种基于声纹信息数据的高压断路器故障识别方法。首先，基于自行研制的声纹信息数据采集装置，获取某高压断路器的声纹信息；其次，文章提出了一种改进的S变换，基于声纹信息的S变换生成幅度矩阵；然后，通过矩阵奇异值分解方法，从时频角度提取声纹信息的故障特征量，建立支持向量机模型诊断系统，并对系统进行训练以实现高压断路器的故障识别；最后，通过实验仿真计算表明，所提故障识别方法在不同工况下的准确率达到了92.6%，验证了所提方法及设备具有良好的准确性和鲁棒性。
