Abstract Transformer oil chromatography analysis is of great significance for the operation and maintenance of the power transformers, and fuzzy clustering algorithm is an important intelligent algorithm for oil chromatographic analysis. However, the traditional fuzzy c-means algorithm (FCM) cannot achieve the fault classification of dissolved gas analysis (DGA) data effectively. The traditional FCM membership function has many local extreme points, and this is obstructive to DGA data classification. This paper reconstructed the membership degree calculation method of fuzzy clustering algorithm. The similarity function of exponential form was constructed, and the membership function with monotonicity of distance was obtained. This method eliminated the local extreme point of membership function. The similarity calculation was divided into two steps. First calculated the sub-similarity according to each attribute of the sample, and then merged them into the final membership. The example shows that the scheme improves the ability of fuzzy clustering algorithm to identify DGA fault pattern, and improves the classification performance of the algorithm.
Li Enwen,Wang Linong,Song Bin等. Analysis of Transformer Oil Chromatography Based on Improved Fuzzy Clustering Algorithm[J]. Transactions of China Electrotechnical Society, 2018, 33(19): 4594-4602.
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2018, Vol. 33 
