Research On Fault Diagnosis Of Power Transformer Based On Neural Network And Evidence Theory

Power transformer is the key apparatus at the transformer substation, its working safe and reliability relate to the safety and stabilization of power system directly. So it is important to study the electric power equipments' condition monitoring and fault diagnosis.This paper is based on a lot of references, system overview the fault processing of the power transformer, the principle of generation characteristic gas, corresponding relationship between the different kinds of fault and different kinds of characteristic gas content and detection method and fault attribute classification technology. Combining the actual work, the main work is as follows:(1) Based on the relationship between the dissolving gas content in oil and different fault types, analyze the sample data of the gas content in oil. Then these data samples become to the learning and training characteristic vector matrix of the artificial neural network. Making the most of the paralleling processing, learning, memorization, nonlinearity mapping, adaptation ability and robustness etc of the artificial neural network, constructing the fault characteristic gas diagnosis system of the power transformer based on the artificial neural network. Selecting and training the BP and RBP neural network which suitable to the power transformer running condition and fault on-line detection, diagnosis and forecasting. We have discussed the network construction, optimization and algorithmic, and we have done the simulation experiment by the NNTOOL in the Matlab. Then studied and solved method has put forward in this paper. The main content to study is definite. Through the comparative of each different type neural network's performance and fault diagnosis's rate of accuracy, it determined the suitable neural network model in the transformer insulation failure diagnosis.(2) Information fusion on characteristic level based on D-S evidence theory: the basic Principle, formatting rule, reasoning Process of evidence theory are introduced in detail. A method of fault diagnosis based on evidence theory and information fusion is Proposed and expounded about how to realize the fault diagnosis system.(3)Evidence theory and neural network are widely used in data fusion systems. However, there exist some problems in its combination rule. This paper quoted a new algorithm for assigning Mass function. This algorithm determines the uncertainty of the bodies of evidence, which combines the reliability of the bodies of evidence and the entropy of associated coefficient between the evidences and the targets. It also generally reflects the total uncertainty of the evidences. Directly toward the algorithm, this paper proposed a new synthetic method of transformer fault diagnosis, which based on the neural networks and D-S evidence theory.(4) Analyzing the effectiveness of a number of transformer fault diagnosis model. The analysis of a large number of examples show that the diagnostic accuracy of the model proposed in this paper was higher than a single diagnostic model, the algorithm has high detection accuracy in the power transformer fault diagnosis. And it has a good application prospects.(5) The above research results are summarized finally. The further investigative direction is put forward in the end.