基于方向梯度直方图属性空间的局部放电模式识别改进算法

doi:10.19595/j.cnki.1000-6753.tces.200327

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Abstract This paper proposed an improved algorithm for partial discharge pattern recognition based on the attribute space of histogram of oriented gradient (HOG), aiming to enhance the summary ability of features and overcome the limitation of traditional classifier for the high-dimensional features. Firstly, the partial discharge phase resolved pulse sequence (PRPS) patterns were constructed as the recognition basis. And the HOG attribute space of PRPS images was formed automatically by the iterative algorithm of local cell superimposed slipped window. Secondly, in order to satisfy the mutual independence, the HOG attribute space was reconstructed by linear transformation of covariance matrix and was rearranged according to attribute importance. Thirdly, the number of attributes input to the Naïve Bayesian classifier was sequentially increased, and then the best attribute subset was obtained based on the classification accuracy. After weighting the reduced attribute according to their relative importance, the HOG attribute selective weighted naïve bayes classifier was finally designed. The test results of a large number of samples prove that the improved algorithm can achieve high recognition accuracy, and has an obvious optimization effect and good application value.

Key words： Partial discharge pattern recognition histogram of oriented gradient phase resolved pluse sequence attribute selective weighted Naïve Bayes

Received: 04 April 2020

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	Song Simeng
	Qian Yong
	Wang Hui
	Sheng Gehao
	Jiang Xiuchen

Cite this article:

Song Simeng,Qian Yong,Wang Hui等. Improved Algorithm for Partial Discharge Pattern Recognition Based on Histogram of Oriented Gradient Attribute Space[J]. Transactions of China Electrotechnical Society, 2021, 36(10): 2153-2160.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.200327 OR https://dgjsxb.ces-transaction.com/EN/Y2021/V36/I10/2153

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