An Artificial Neural Network Model with Yager Composition Theory for Transformer State Assessment
Oil-immersed transformer is one of the key equipment in power system.The state assessment is an important mean to ensure the normal operation of power transformer.Traditional transformer state assessment uses a single standard,which cannot get the ideal effect.In addition,different types of status information in power transformer gives different evaluation results,which makes equipment management personnel difficult to make decision.Taking into account the these defects,this paper proposes an artificial neural network model with Yager composition theory for transformer state assessment,which chooses the raw data of state variables as the input of artificial neural network,and the output vectors of artificial neural network are used as the preliminary evaluation results.As for the conflicting evaluation results from different kinds of evidence,this paper uses the Yager evidence synthesis theory to fuse the various evaluation results of the transformer status.Related experiments based on the established model are performed on multiple sets of samples.The results show that this artificial neural network model with Yager composition theory has a better effect than traditional models.
data fusion power transformer state assessment artificial neural network yager composition theory
Zhiming Lin Songping Tang Gang Peng Yun Zhang Zhenxin Zhong
Guangdong Power Grid Corporation Huizhou Power Supply Bureau,516000,Huizhou Guangdong,China
国际会议
重庆
英文
652-655
2017-03-25（万方平台首次上网日期，不代表论文的发表时间）