Pattern Recognition of Partial Discharge in the Presence of Noise Based on Speeded up Robust Features

被引：0

作者：

Li Z. ^{[1
]}

Wang H. ^{[1
]}

Qian Y. ^{[1
]}

Huang R. ^{[2
]}

Cui Q. ^{[2
]}

机构：

[1] Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai

[2] State Grid Shandong Electric Power Company, Jinan

来源：

Diangong Jishu Xuebao/Transactions of China Electrotechnical Society | 2022年 / 37卷 / 03期

关键词：

Bacterial foraging optimization algorithm; Feature extraction; Partial discharge; Speeded up robust features(SURF); Support vector machine;

D O I：

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

中图分类号：

学科分类号：

摘要：

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. © 2022, Electrical Technology Press Co. Ltd. All right reserved.

引用

页码：775 / 785

页数：10

共 28 条

[1]

Zhang Liangen, Lu Shijie, Li Chengrong, Et al., Motor behavior and hazard of spherical and liner particle in gas insulated switchgear, Transactions of China Electrotechnical Society, 34, 20, pp. 4217-4225, (2019)

[2]

Tang Zhiguo, Tang Mingze, Li Jinzhong, Et al., Review on partial discharge pattern recognition of electrical equipment, High Voltage Engineering, 43, 7, pp. 2263-2277, (2017)

[3]

Zhu Yufeng, Xu Yongpeng, Chen Xiaoxin, Et al., Pattern recognition of partial discharges in DC XLPE cables based on convolutional neural network, Transactions of China Electrotechnical Society, 35, 3, pp. 659-668, (2020)

[4]

Rudd S, McArthur S D, Judd M D., A generic knowledge-based approach to the analysis of partial discharge data, IEEE Transactions on Dielectrics and Electrical Insulation, 17, 1, pp. 149-156, (2010)

[5]

Zang Yiming, Wang Hui, Qian Yong, Et al., GIL partial discharge localization method based on 3D optical fingerprint and NPSO-KELM, Proceedings of the CSEE, 40, 20, pp. 6754-6764, (2020)

[6]

Deng Ran, Zhu Yongli, Liu Xuechun, Et al., Pattern recognition of unknown types in partial discharge signals based on variable predictive model and tanimoto, Transactions of China Electrotechnical Society, 35, 14, pp. 3105-3115, (2020)

[7]

Lu Fangcheng, Jin Hu, Wang Zijian, Et al., GIS partial discharge pattern recognition based on principal component analysis and multiclass relevance vector machine, Transactions of China Electrotechnical Society, 30, 6, pp. 225-231, (2015)

[8]

Qin Xue, Qian Yong, Xu Yongpeng, Et al., Application of feature extraction method based on 2D-LPEWT in cable partial discharge analysis, Transactions of China Electrotechnical Society, 34, 1, pp. 170-178, (2019)

[9]

Gao Jiacheng, Zhu Yongli, Zheng Yanyan, Et al., Pattern recognition of partial discharge based on VMD-WVD and SSAE, Proceedings of the CSEE, 39, 14, pp. 4118-4129, (2019)

[10]

Majidi M, Fadali M S, Etezadi-Amoli M, Et al., Partial discharge pattern recognition via sparse representation and ANN, IEEE Transactions on Dielectrics and Electrical Insulation, 22, 2, pp. 1061-1070, (2015)

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