Dissolved Gas Analysis In Oil-based Transformer Fault Prediction

The power transformer is one of the most important electrical equipments in power system, whether dose it run safely and stably will affect the power reliable supply and system normal operation. Because of the influence coming from outside or human factors, the transformer often breaks down. Therefore, this article mainly solved how to use the technology of DGA and the oil dissolved gas history data to forecast transformer's future condition. Thus could detect latter faults in advance, reduce the occurrence of accidents, and provide the policy-making support to repairers. In view of the oil dissolved gas system was a typical gray system, this article based on the research of ordinary gray forecast model GM(1, 1), combined with actual project requirements, proposed concrete improved algorithm to GM(1, 1) model. Taking the same time dissolved gas data as the model, carried on one time exponential operation and transformd the background value. Then through circularly calculating and selected a model which has the smallest error as the best prediction model. Moreover, in view of the gray forecast theory and the neural network having supplementary characteristic, integrated the gray forecast theory and the neural network to construct the Gray Neural Network Model GNNM(1, 1), which fully excavated the gray forecast theory and the neural network respective merits. The examples proved that, the above methods could be successfully used to predict the chromatogram trend of transformer, which had high accuracy and had certain project application value. Finally, played on MATLAB M language designing a transformer condition forecast software based on dissolved gas. This software had user-friendly interface and perfect data output function, which set up regular GM(1, 1) model, improved GM(1, 1) model, Gray Neural Network GNNM(1, 1) model, and two diagnosis model——the improved IEC diagnosis model and the neural network diagnosis model. Through diagnosing each forecast model's data, and carrying on the overall evaluation to each kind of diagnosis results, final results could tell us transformer's future condition, including whether there were faults or faults'types and typical reasons for them. When according to the forecast data diagnosed that transformer existed faults, this system could realize auto-alarm.