Assessing Approach Of Transformer Condition

Transformer condition assessment is the basis of the transformer condition based maintenance. In this paper we have done in-depth research in transformer fault diagnosis, fault forecasting and condition assessment. The main outcome of our research includes: 1. In our research, the Bayesian network classifier is firstly used in the transformer fault diagnosis. We develops three classifier models: NB, TAN and BAN classifier, which hold the high accuracy when there is not much information lost for Bayesian classifier is able to handle incomplete information, in transformer fault diagnosis. 2. To solve the low accuracy rate in above three models when key attribute information is lost, we devise a new method, which combines the Bayesian classifier and the rough set for the transformer fault diagnosis. The three classifier models namely NB rough set, TAN rough set and BAN rough set have the function of self-study, which can still get the high accuracy rate even when key attribute information is lost. 3. Aiming at the non-equal-gap characteristics of the transformer experiments, we raise a approach for the transformer all-purpose non-equal-gap grey fault prediction and develop several non-equal-gap grey forecasting models, including the improved transformer fault forecasting grey non-equal-gap GM (1,1) model and the improved non-equal-gap grey Verhulst prediction model. We not only take the non-equal-gap property of the predicting sequence into consideration, but also make improvements in the selection of initial conditions, the modification of background value, etc. Experiments show that these models have comparatively higher accuracy. We develop the grey models group prioritizing forecasting model for transformer fault prediction. The model has higher prediction accuracy. 4. We propose the method for Bayesian network based transformer synthesized condition evaluation and devise the Bayesian network condition evaluation model. This method combines the transformer history condition, the current condition and the forecasting condition to determine the synthesized condition, which is helpful in providing support for transformer condition maintenance. 5. We design a transformer condition evaluation system framework based on data mining and data mart techniques. The transformer condition information scattered in different company departments is gathered through data mart and the structure of condition evaluation sub-system is built by using OAA. Therefore the framework is open and flexible, easy for development and maintenance.