China Electric Power Research Institute, Beijing 100192, China.
State Grid Zhejiang Electric Power Research Institute, Hangzhou 310014, China.
Sensors (Basel). 2023 Mar 20;23(6):3258. doi: 10.3390/s23063258.
A transformer's acoustic signal contains rich information. The acoustic signal can be divided into a transient acoustic signal and a steady-state acoustic signal under different operating conditions. In this paper, the vibration mechanism is analyzed, and the acoustic feature is mined based on the transformer end pad falling defect to realize defect identification. Firstly, a quality-spring-damping model is established to analyze the vibration modes and development patterns of the defect. Secondly, short-time Fourier transform is applied to the voiceprint signals, and the time-frequency spectrum is compressed and perceived using Mel filter banks. Thirdly, the time-series spectrum entropy feature extraction algorithm is introduced into the stability calculation, and the algorithm is verified by comparing it with simulated experimental samples. Finally, stability calculations are performed on the voiceprint signal data collected from 162 transformers operating in the field, and the stability distribution is statistically analyzed. The time-series spectrum entropy stability warning threshold is given, and the application value of the threshold is demonstrated by comparing it with actual fault cases.
变压器的声信号包含丰富的信息。在不同的运行条件下，声信号可以分为暂态声信号和稳态声信号。本文分析了振动机理，基于变压器出线套管跌落缺陷挖掘声学特征，实现了缺陷识别。首先，建立了质量-弹簧-阻尼模型，分析了缺陷的振动模式和发展模式。其次，对声纹信号进行短时傅里叶变换，利用梅尔滤波器组对时频谱进行压缩感知。然后，将时间序列谱熵特征提取算法引入稳定性计算中，并通过与模拟实验样本进行比较来验证该算法。最后，对现场运行的 162 台变压器的声纹信号数据进行稳定性计算，并对稳定性分布进行统计分析。给出了时间序列谱熵稳定性预警阈值，并通过与实际故障案例进行比较，验证了阈值的应用价值。
