A Combination Approach for Transient Power Quality Disturbance Recognition

A combination approach of wavelet transformation and neural network is applied to realize transient power quality disturbance signals´ recognition. Firstly, the mathematical models of five kinds of transient disturbance signals, such as voltage surging, voltage sag, voltage interruption, transient pulse and transient oscillation are founded. Then, using time-frequency characteristic of wavelet, the sample signal´s feature vectors are extracted. At last these feature vectors are input into BP neural network. Using self-learning ability, the disturbance signals can be classified and recognized. The examples show that the method has a higher discrimination. It´s effective to resolve transient power quality problem.