A DSP-based Fault Diagnosis For Power Electronic Circuit Using Neural Network

The concept and development of fault diagnosis technology are first introduced in the beginning of the dissertation. Then significance of fault diagnosis for power electronic circuits and development of fault diagnosis technology for power electronic circuits are clarified. The weakness of them is analyzed. Finally the new fault diagnosis method employing neural network for power electronics circuit is presented.Based on the achivement that previous scholars make, the method of fault diagnosis for power electronic circuit employing frequency analysis and neural network is presented in the dissertation. The two methods of BP neural network and RBF neural network are employed in the fault diagnosis for power electronic circuits. First the simulation model of the being diagnosed system is constructed by the simulation software of MATLAB. By simulating all kinds of faults of the actual system on the simulation model, the training sets is obtained to train the structure of neural network. After studying the effect of the initial weight, the number of nodes and layers and learning rate on neural network, a well designed training sets can be achieved. The well designed training set is beneficial to training and improving generalization capability of neural network. Finally an on-line detection and fault diagnosis system for power electronics inverter circuits based on DSP is introduced in the dissertation.The trained system of neural network for fault diagnosis is verified by simulation and experiment system. Studying the result shows that new fault diagnosis method employing BP and RBF neural network for power electronics inverter circuit is effective, and BP neural network is better than RBF neural network for fault diagnosis of actual project.