A Classification Method for Complex Power Quality Disturbances Using EEMD and Rank Wavelet SVM

Abstract

This paper aims to develop a combination method for the classification of power quality complex disturbances based on ensemble empirical mode decomposition (EEMD) and multilabel learning. EEMD is adopted to extract the features of complex disturbances, which is more suitable to the nonstationary signal processing. Rank wavelet support vector machine (rank-WSVM) is proposed to apply in the classification of complex disturbances. First, the characteristic quantities of complex disturbances are obtained with EEMD through defining standard energy differences of each intrinsic mode function. Second, after the optimization of rank-SVM, based on wavelet kernel function, the ranking function, and multilabel function are, respectively, constructed. Lastly, rank-WSVM is applied to classify the complex disturbances. Simulation results and real-time digital simulator tests show that for different signal to noise ratio, the rank-WSVM classification performance of complex disturbances including hamming loss, ranking loss, one-error, coverage, and average precision, is generally better than the other three methods, namely rank-SVM, multilabel naive Bayes, and multilabel learning with backpropagation.