Study On Power Transformer Fault Diagnosis And Prediction Based On Layered Characteristics Of Fault Feature Gases

Power transformer is the crucial device of energy conversion and transmission in the power grid, it's also the most important electric device, its in-service state directly affects the safety and stability of the power system. The failure of power transformer may cause huge economic loss. The transformer fault diagnosis and fault prediction is the basis of keeping it running normally and carrying out the condition based maintenance, which was studied in-depth in this paper. The main contribution of this thesis was the following.①the layered fault diagnosis model was proposed based on the analysis of the character of transformer fault diagnosis, the layered diagnosis model should be constructed with the optimal features which are selected according to the effective information amount. The optimal features are the feature subset which may provide the most classification information with the least redundancy.②application of mutual information to feature selection for transformer fault diagnosis, to overcome the drawbacks in the calculation method of mutual information, an improved calculation method was proposed. As mutual information is not suited to evaluate the mutual information between a feature and a class variable, mutual information was replaced with chi-square distance, which was modified to have the same order of magnitude as mutual information. The feature selection method proposed here used modified chi-square distance to evaluate the effective information provided by the feature, used mutual information to evaluate the redundancy degree between features, selected the optimal feature subset for each layer.③a fuzzy mathematic interpretation of neural networks was given, and based on this interpretation, a automatic design and initialization of neural networks was proposed. First this approach analyzed the relationships between feature subsapces and fault types, gave the definition of the chi-square relation, then constructed and initialized the neural network based on the chi-square relations.④to improve the performance and the anti-noise ability of transformer fault diagnosis, neural network ensemble method was applied, this paper analyzed the ensemble structure of layered fault diagnosis, applied kernel PCA to increase the number of features, compared the kernel eigen value, eigen feature and its classification information, the experiment results showed that the eigen value didn't reflect the amount of effective information, the neural network ensemble was constructed by optimal kernel features which were selected according to modified chi-square distance and mutual information.⑤analyzed the characters of the prediction of DGA gas concentrations in transformer, the key issue of gas concentration prediction is the prediction of the change of gas concentrations, so the change series was used to construct prediction model, the change series was pre-processed with the deviation of gas concentration series, this pre-precession eliminated the influence to the prediction brought by the magnitude differences among concentrations of different gases,⑥As in a transformer, there are mutual affections among feature gases, so individually developing prediction models for each gas is not appropriate, fuzzy cognitive map method was applied to construct a universal prediction model for all feature gases in a transformer. This method could learn the behavior rules of a given system from its history data, to some extent this prediction model achieved the long tern prediction.