Hybrid Model of Power Transformer Fault Classification Using C-set and MFCM – MCSVM  

作　　者：Ali Abdo Hongshun Liu Yousif Mahmoud Hongru Zhang Ying Sun Qingquan Li Jian Guo 

机构地区：[1]Shandong Provincial Key Laboratory of UHV Transmission Technology and Equipment,School of Electrical Engineering,Shandong University,Jinan 250061,China

出　　处：《CSEE Journal of Power and Energy Systems》2024年第2期672-685,共14页中国电机工程学会电力与能源系统学报（英文）

基　　金：supported by the National Natural Science Foundation of China under grant Ui966209;Natural Science Foundation of Shandong Province under grant ZR2020ME196.

摘　　要：This paper aims to increase the diagnosis accuracy of the fault classification of power transformers by introducing a new off-line hybrid model based on a combination subset of the et method(C-set)&modified fuzzy C-mean algorithm(MFCM)and the optimizable multiclass-SVM(MCSVM).The innovation in this paper is shown in terms of solving the predicaments of outliers,boundary proportion,and unequal data existing in both traditional and intelligence models.Taking into consideration the closeness of dissolved gas analysis(DGA)data,the C-set method is implemented to subset the DGA data samples based on their type of faults within unrepeated subsets.Then,the MFCM is used for removing outliers from DGA samples by combining highly similar data for every subset within the same cluster to obtain the optimized training data(OTD)set.It is also used to minimize dimensionality of DGA samples and the uncertainty of transformer condition monitoring.After that,the optimized MCSVM is trained by using the(OTD).The proposed model diagnosis accuracy is 93.3%.The obtained results indicate that our model significantly improves the fault identification accuracy in power transformers when compared with other conventional and intelligence models.

关 键 词：Combination subset of set(C-set)method modified fuzzy C-means(MFCM) optimizable multiclass-SVM(MCSVM) optimized training data(OTD) 

分 类 号：TM41[电气工程—电器]