基于改进经验小波变换和XGBoost的电能质量复合扰动分类

doi:10.19595/j.cnki.1000-6753.tces.201363

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Abstract Aiming at the shortcomings of multiple power quality disturbances classification method in terms of classification number and classification performance, a recognition method of multiple power quality disturbances based on modified empirical wavelet transform (MEWT) and extreme gradient boosting (XGBoost) was proposed. Firstly, the traditional empirical wavelet transform was improved to make it suitable for feature extraction of multiple disturbances. Then, according to the MEWT analysis results of basic disturbances, feature sequences which can effectively depict different disturbance characteristics were extracted from time and frequency domain. Finally, a multi label classification model for multiple disturbances with XGBoost as sub classifier was constructed based on the problem transformation strategy, and the model training method combining feature selection and hyperparameter optimization was used to further improve the effect of classification. The experimental results show that the proposed method can effectively identify 48 types of disturbances. Compared with the traditional multi label disturbance classification method, the proposed method performs better in classification accuracy and noise robustness, and has faster operation speed, which is suitable for engineering practice.

Key words： Multiple power quality disturbances empirical wavelet transform scale-space representation multi label classification XGBoost

Received: 12 October 2020

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TM76

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	Wu Jianzhang
	Mei Fei
	Zheng Jianyong
	Zhang Chenyu
	Miao Huiyu

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Wu Jianzhang,Mei Fei,Zheng Jianyong等. Recognition of Multiple Power Quality Disturbances Based on Modified Empirical Wavelet Transform and XGBoost[J]. Transactions of China Electrotechnical Society, 2022, 37(1): 232-243.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.201363 OR https://dgjsxb.ces-transaction.com/EN/Y2022/V37/I1/232

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