Application of Wavelet Network for Automatic Power Quality Disturbances Recognition in Distribution Power System

Power quality (PQ) has attracted considerable attention from both utilities and users due to the use of many types of sensitive electronic equipment. This paper proposed a novel approach for the PQ disturbances classification based on the wavelet network. Wavelet transform is utilized to extract feature vectors for various PQ disturbances based on the multi-resolution analysis (MRA). These feature vectors then are applied to wavelet network for training and testing. The signal containing noise is de-noised by wavelet transform to obtain a signal with higher signal-to-noise ratio (SNR). The synthesized method of recursive orthogonal least squares algorithm (ROLSA) and improved Givens transform is used to fulfill the network structure. The fundamental component of the signal is estimated to extract the mixed information using wavelet network, and then the disturbance is acquired by subtracting the fundamental component. The simulation results demonstrate that the proposed method is effective. Compared with conventional methods, the simulation results show accurate discrimination, fast learning, good robustness, and faster processing time for detecting PQ disturbing.