Fault Attribute Reduction of Oil Immersed Transformer Based on Improved Imperialist Competitive Algorithm

作     者：Li Bian Hui He Hongna Sun Wenjing Liu Li Bian;Hui He;Hongna Sun;Wenjing Liu

作者机构：College of Electronic and Information EngineeringGuangdong Ocean UniversityZhanjiang 524088GuangdongChina College of Electrical and Control EngineeringHeilongjiang University of Science and TechnologyHarbin 150022China Handan Power Supply CompanyHandan 056002HebeiChina 

出 版 物：《Journal of Harbin Institute of Technology(New Series)》 (哈尔滨工业大学学报（英文版）)

年 卷 期：2020年第27卷第6期

页      面：83-90页

学科分类：0810[工学-信息与通信工程] 080801[工学-电机与电器] 0808[工学-电气工程] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Sponsored by the National Natural Science Foundation of China(Grant No.51504085) the Natural Science Foundation for Returness of Heilongjiang Province of China(Grant No.LC2017026). 

主　　题：transformer fault improved imperialist competitive algorithm rough set attribute reduction BP neural network 

摘      要：The original fault data of oil immersed transformer often contains a large number of unnecessary attributes,which greatly increases the elapsed time of the algorithm and reduces the classification accuracy,leading to the rise of the diagnosis error rate.Therefore,in order to obtain high quality oil immersed transformer fault attribute data sets,an improved imperialist competitive algorithm was proposed to optimize the rough set to discretize the original fault data set and the attribute reduction.The feasibility of the proposed algorithm was verified by experiments and compared with other intelligent algorithms.Results show that the algorithm was stable at the 27th iteration with a reduction rate of 56.25%and a reduction accuracy of 98%.By using BP neural network to classify the reduction results,the accuracy was 86.25%,and the overall effect was better than those of the original data and other algorithms.Hence,the proposed method is effective for fault attribute reduction of oil immersed transformer.