A Fault Diagnosis Method For Four-Phase Rectifier Based The Artificial Neural Network

The electric and electronic diagnosis technology influences to deep in today’s power system, industrial automation, computer, electronic information, etc duing to the power electronic and power device of the wide application and fast development. The research on the electrical power electronic fault diagnosis is of great significance. This topic is new type of four phase rectifier as the research object, with the focus on the fault diagnosis algorithms based on artificial neural network.This paper first researches the model circuit:four phase full controlled rectifier bridge of the specific work mode, the failure forms and species for a comprehensive forecast and statistical finishing.Secondly, we select different test points and different pattern recognition networks for fault diagnosis. Through the Matlab platform on four-phase full controlled rectifier bridge circuit, we get a lot of experimental data to establish the fault diagnosis system.Then, We establish the fault diagnosis system. Primary diagnosis system bases on BP neural network. BP network diagnostic uses DC output voltage as fault testing point, make spectrum analysis for processing after Normalizing Acquisition of data, then use the transformed data to make BP network discriminant analysis. Secondary diagnosis system respectively bases on BP network and the Elman network diagnosis method. We use DC voltage of the wavelet analysis as fault data to achieve the pattern recognition through the BP network and Elman network algorithm for fault data analysis. Finally, through test and comparison of two methods of fault diagnosis, Shows that the BP network can not meet the specified accuracy in the secondary diagnosis. And based on the Elman network fault diagnosis system for pattern recognition when data set Differentiation is not obvious, it has Recognition ability, high accuracy to achieve the desired effect.