基于加速鲁棒特征的含噪局部放电模式识别

doi:10.19595/j.cnki.1000-6753.tces.210018

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Abstract Due to the complex environmental impacts, the patrial discharge (PD) data obtained at substation always contain lots of noisy signals. To improve the accuracy of PD recognition, a PD pattern recognition method based on speeded up robust features (SURF) and improved support vector machine (BFO-SVM) is proposed. Contaminated PD data were made by fusing the pure PD data with noise and the phase resolved pulse sequence (PRPS) patterns were constructed. Then the SURF algorithm was used to extract the feature points and feature descriptors of the PRPS grayscale images automatically. After that, visual word frequency features of different PD types were generated by using bag-of-words and K-means clustering method. The features were input into the BFO-SVM classifier, and the recognition results were contrasted with those acquired from the gray gradient co-occurrence matric (GLCM) and the traditional SVM optimization algorithm. Results show that the algorithm has high recognition accuracy and strong anti-interference ability under high-amplitude white noise background and typical interference environment. The finding results can be used as reference for PD detection and identification on the spot.

Key words： Partial discharge feature extraction speeded up robust features(SURF) bacterial foraging optimization algorithm support vector machine

Received: 07 January 2021

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	Li Ze
	Wang Hui
	Qian Yong
	Huang Rui
	Cui Qihui

Cite this article:

Li Ze,Wang Hui,Qian Yong等. Pattern Recognition of Partial Discharge in the Presence of Noise Based on Speeded up Robust Features[J]. Transactions of China Electrotechnical Society, 2022, 37(3): 775-785.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.210018 OR https://dgjsxb.ces-transaction.com/EN/Y2022/V37/I3/775

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