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

Ali Abdo; Hongshun Liu; Yousif Mahmoud; Hongru Zhang; Ying Sun; Qingquan Li; Jian Guo

doi:10.17775/CSEEJPES.2020.04010

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

Regular Papers | 更新时间：2026-10-15

- Hybrid Model of Power Transformer Fault Classification Using C-set and MFCM – MCSVM
- CSEE Journal of Power and Energy Systems Vol. 10, Issue 2, Pages: 672-685(2024)
- 作者机构：
  
  Shandong Provincial Key Laboratory of UHV Transmission Technology and Equipment, School of Electrical Engineering, Shandong University,Jinan,China
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2020.04010
  CLC：
- Published：01 March 2024
- 稿件说明：
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Ali Abdo, Hongshun Liu, Yousif Mahmoud, et al. Hybrid Model of Power Transformer Fault Classification Using C-set and MFCM – MCSVM[J]. CSEE Journal of Power and Energy Systems, 2024, 10(2): 672-685.
DOI：

Ali Abdo, Hongshun Liu, Yousif Mahmoud, et al. Hybrid Model of Power Transformer Fault Classification Using C-set and MFCM – MCSVM[J]. CSEE Journal of Power and Energy Systems, 2024, 10(2): 672-685. DOI： 10.17775/CSEEJPES.2020.04010.

摘要

Abstract

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.

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