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《Proceedings of the Chinese Society of Universities for Electric Power System and its Automation》 2008-06
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Transformer Condition Assessment Based on Support Vector Machine and DGA

ZHU Yong-li,SHEN Tao,LI Qiang(School of Electrical and Electronic Engineering,North ChinaElectric Power University,Baoding 071003,China)  
Aiming at the problem that power transformer ageing,fault mechanism are complex and their conditions are difficult to evaluate accurately,this paper presents a method for evaluating the condition of the oil-filled transformer based on support vector machine and data of dissolved gasses aralysis(DGA). This model takes the gas content and gas production rate in transformer oil dissolved gas as the evaluation indicators. Combined with《Preventive test electrical equipment order》and《Transformer oil dissolved gas analysis and judgment guide》semi-trapezoid percentile score model is developed for the evaluation of selected indicators. With this model the transformer condition is into four grades,fine,general,pay attention to and relatively poor.the support vector machine (SVM) multi-classifer is trained by samples classfied from the historical experiment database of transformers.The trained SVM multi-classifier can correctly determine the current state of a transformer. The results of practical case verify the accuracy and the practicability of the proposed idea.
【CateGory Index】: TM411
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