Improved Fuzzy C-Means Clustering for Transformer Fault Diagnosis Using Dissolved Gas Analysis Data

Enwen Li, Linong Wang, Bin Song and Siliang Jian
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Enwen Li: School of Electrical Engineering, Wuhan University, Wuhan 430072, China
Linong Wang: School of Electrical Engineering, Wuhan University, Wuhan 430072, China
Bin Song: School of Electrical Engineering, Wuhan University, Wuhan 430072, China
Siliang Jian: School of Electrical Engineering, Wuhan University, Wuhan 430072, China

Energies, 2018, vol. 11, issue 9, 1-17

Abstract: Dissolved gas analysis (DGA) of the oil allows transformer fault diagnosis and status monitoring. Fuzzy c-means (FCM) clustering is an effective pattern recognition method, but exhibits poor clustering accuracy for dissolved gas data and usually fails to subsequently correctly classify transformer faults. The existing feasible approach involves combination of the FCM clustering algorithm with other intelligent algorithms, such as neural networks and support vector machines. This method enables good classification; however, the algorithm complexity is greatly increased. In this paper, the FCM clustering algorithm itself is improved and clustering analysis of DGA data is realized. First, the non-monotonicity of the traditional clustering membership function with respect to the sample distance and its several local extrema are discussed, which mainly explain the poor classification accuracy of DGA data clustering. Then, an exponential form of the membership function is proposed to obtain monotony with respect to distance, thereby improving the dissolved gas data clustering. Likewise, a similarity function to determine the degree of membership is derived. Test results for large datasets show that the improved clustering algorithm can be successfully applied for DGA-data-based transformer fault detection.

Keywords: fuzzy c-means clustering; dissolved gas analysis; membership function; degree of membership; similarity measurement; transformer fault (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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