基于稀疏自适应S变换的变压器短路冲击绕组状态声信号检测

Acoustic Signal Detection of Transformer Winding Under Short-circuit Impact Based on Sparse Adaptive S-transform

针对变压器遭受短路冲击时的声信号,该文提出一种基于稀疏自适应S变换的变压器绕组状态检测方法。首先依据变压器短路冲击声信号的幅值谱确定了稀疏自适应S变换的窗函数,然后采用双向2维主成分分析对声信号时频特征矩阵进行了降维处理,据此定义了特征能量变化率来评判变压器的绕组状态。对某220kV变压器短路冲击声信号的计算结果表明:基于稀疏自适应S变换得到的声信号优化时频域特征具有分辨率高、能量聚集性强的优点,所定义的特征指标能有效反映变压器绕组状态变化。研究结果可为变压器短路冲击绕组状态检测提供重要依据。

Based on the acoustic signals of a power transformer under the short-circuit impact, a detection method of the transformer winding condition based on the sparse adaptive S transform is proposed in the paper. First, the window function of the sparse adaptive S transform is determined according to the amplitude spectrum of the acoustic signals of the transformer under the short-circuit impact. Then the two-dimensional principle component analysis(2 DPCA) method is used to reduce the dimension of the time-frequency feature matrix. The energy change rate of the features is finally defined to detect the winding condition of the transformer. The calculation results of the acoustic signals of a 220 k V transformer under the short-circuit impulse test show that the optimized time-frequency features of the acoustic signals of the transformer under the short-circuit impulse obtained by the sparse adaptive S transform have the advantages of high resolution and strong energy concentration. The energy changing ratio of the time-frequency feature matrix can effectively reflect the variations of the transformer winding condition. The research results can provide an important basis for the detection of the transformer short-circuit impulse winding state.