Application of multi-label classification method to catagorization of multiple power quality disturbances

A new method of identifying the catagory of multiple power quality disturbances based on multi-label classification was presented. A multi-label ranking learning method named k-nearest neighbor Bayesian rule (KNN-Bayesian) was designed based on k-nearest neighbor and Bayesian methods. Firstly, several common power quality disturbances and their compound ones were decomposed by discrete wavelet transform, and the norm energy entropy of the wavelet coefficients of each level were extracted as eigenvectors. And then, the disturbances were classified using KNN-Bayesian. The simulation results show that KNN-Bayesian can recognize the multiple power quality disturbances including voltage sag, voltage swell, interruption, impulsive transient, harmonics, flicker and their compound ones effectively under different disturbance conditions. © Chin. Soc. for Elec. Eng.