Abstract:In order to realize the quantitative analysis and prediction on the operation state of the transformer, the interchange complex matter element was built between dissolved gases in transformer oil and typical faults. Analytic Hierarchy Process (AHP) and maximum information entropy were used to determine the subjective and objective weights influencing the transformer health level, respectively. The quantitative analysis of the transformer health level was proposed based on matter element maximum information entropy. The Support Vector Machines (SVM) algorithm was adopted to predict the operation condition of transformers, the parameters （c and g） were optimized by grid-search, Genetic Algorithm (GA) and Particle Swarm Optimization（PSO），and the optimal prediction model was established. This method provides a good guiding value for the elimination of transformer faults, overhaul decisions and online predictions.

Abstract:With in mind the inverse model identification problems in inverse system method,this paper studied the realization of inverse system identification and control using support vector machines(SVM),and proposed a compound control strategy combing SVM inverse