Wavelet-matrix transforming method for similarity measurement of fault waveform of electronic power devices
电力电子装置故障波形相似性度量的小波矩阵变换法

Based on the wavelet and the matrix transformation, we propose a method for measuring the time series similarity for application in the fault waveform similarity of electronic power devices. The noise-rejection ability, the sensitivity and the accuracy of this method are discussed. By using the wavelet transformation, we compress the time-series sequence into the wavelet subspace. The sample's feature vector and the orthogonal basis of the sampled time-series sequence are obtained by K-L transformation(Karhunen-Loeve transformation). By taking the inner-product, the analyzed time-series sequence is projected into the orthogonal basis, and the analyzed feature vector is thus obtained. Finally, the similarity value is calculated by the Euclid distance between the sample's feature vector and the analyzed feature vector. In the measurement of the similarity between the fault waveforms in electronic power devices, the experimental results show that the dimension of feature vectors is low by the proposed method. In addition, the noise-rejection ability of the proposed method is 30 times higher than that of the plain wavelet method, the sensitivity of the proposed method is 1/3 of that of the plain wavelet method, and the accuracy of similarity value of the proposed method is higher than that of the wavelet singular-value-decomposition method. The proposed method has potential value in similarity matching and indexing for lager time-series databases.