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In view of artificial neural network, there are some deficiencies in condenser fault diagnosis. The BP neural network used for condenser fault diagnosis is highly nonlinear pattern recognition and high precision of fault diagnosis. The PSO-BP neural network can effectively solve the problem of BP neural network that training time is long and training process is easy to fall into the local minimum. The training results of PSO-BP network in convergence speed and conver- gence effect are significantly improved. Under the condition of small samples, the calculation results of SVM method are better than the calculation result of the other two methods. Although the recognition ratio of improved PSO-BP(2) and SVM is the same, training time of improved PSO-BP(2) is longer than training time of SVM. The generalization ability of SVM is stronger, and the efficiency of SVM is higher than the neural network. With MATLAB programming, three dif- ferent algorithms, which are BP neural network, PSO-BP neural network and SVM, are studied and compared for the per- formance of condenser fault diagnosis. In the models of this study, the research results show that condenser fault diagno- sis based on SVM has the fastest convergence speed and the best accuracy.

A Comparative Research on Condenser Fault Diagnosis Based on Three Different Algorithms