Transformer Fault Diagnosis Expert System

Power transformer fault diagnosis is vital to power system safe operating. After discussing transformer fault cause, fault type and diagnostic procedure, this paper summarizes existing diagnosis method and puts forward idea of transformer fault diagnosis expert system based on multilayer distributed reasoning mechanism on the basis of collecting extensively expert knowledge and experience. The dissolve gas analysis method which adopted widely at home and abroad is used as dominant idea to diagnose internal incipient fault. Synthetical diagnosis on the basis of combining all kinds of relevant tests make a decision for the fault position. Tests include routine test for oil and preventive test for transformer. The knowledge base includes diversified rules, such as basic and complex rule composed of vary tests item, diagnosis rule that distributed impliedly over combined chaos neural network and the transformer fault case. It supply a gap of single way to adopt the reasoning mechanism cooperated with case-based reasoning and the combination of positive reasoning and chaos neural network model. After researching the application of artificial neural network to fault diagnosis of transformer, the back propagation algorithm is improved observably. Inducting chaos dynamics principium to create chaos neural network back propagation algorithm and using the pretreatment of training sample, the emulation program show that this method is effective. Combining the software engineering project of Heze power supply bureau, the system software is developed which based on browser/server structure. A condition based maintenance transformer fault diagnosis expert system software on internet is presented.