Transient Power Quality Recognition Based on BP Neural Network Theory

摘要

This paper researches on the combination between wavelet transformation and neural network to realize the 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 the nice 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 the nice 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.