An intelligent system for automatic partial discharge pattern recognition is proposed using adaptive optimal kernel time-frequency representation and a fuzzy k-nearest neighbor classifier. The adaptive optimal kernel technique is employed to acquire the joint time-frequency information for partial discharge signals, which is characterized by the adaptive optimal kernel amplitude matrix. A new feature extraction algorithm, i.e., non-negative matrix factorization aided principal component analysis, is proposed to solve the difficulties of principal component analysis for feature extraction of partial discharge adaptive optimal kernel amplitude matrices due to the high dimensionality. Using an ultra-high frequency detector, 600 partial discharge signals sampled from 4 categories of typical artificial defect models in the laboratory are employed for testing. It is shown that the maximum classification accuracy of 94.33% is obtained considering different non-negative matrix factorization parameter r and various non-negative matrix factorization iterations T. Also, the classification performance of the non-negative matrix factorization-principal component analysis features is superior to that of principal component analysis features extracted from original partial discharge signals, two-dimensional non-negative matrix factorization features and phase-resolved partial discharge statistical operators. The proposed technique can be used for partial discharge pattern recognition based on ultra-high-frequency detection arrangements.


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    Title :

    Adaptive Optimal Kernel Time-Frequency Representation Technique for Partial Discharge Ultra-high-frequency Signals Classification




    Publication date :

    2015




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English



    Classification :

    BKL:    53.33 / 53.33 Elektrische Maschinen und Antriebe



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