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Classification of Multiple Power Quality Disturbances by Tunable-Q Wavelet Transform with Parameter Selection

Author

Listed:
  • Lin Yang

    (College of Electrical Engineering, Sichuan University, Chengdu 610065, China)

  • Linming Guo

    (College of Electrical Engineering, Sichuan University, Chengdu 610065, China)

  • Wenhai Zhang

    (College of Electrical Engineering, Sichuan University, Chengdu 610065, China)

  • Xiaomei Yang

    (College of Electrical Engineering, Sichuan University, Chengdu 610065, China)

Abstract

Identifying power quality (PQ) disturbances is an important prerequisite for developing mitigation measures to improve PQ. However, the coupling of multiple PQ disturbances in the noise condition makes it difficult to achieve effective feature extraction and classification. This article proposes a novel method to identify multiple PQ disturbances by integrating improved TQWT with XGBoost algorithm. The improved TQWT is proposed to automatically select the proper tuning parameters by screening the spectral information of PQ signals. Then, the improved TQWT is used to decompose PQ disturbances into sub-bands for further feature extraction. Optimum feature selection and classification are implemented in XGBoost. Classification accuracies of 26 categories of synthetic PQ disturbances under different noisy levels are tested and compared with existing methods. Results indicate that the proposed method is efficient and noise-resistant, and the classification accuracy can reach 97.63% under 20 dB noise, and keep above 99% under lower level noise.

Suggested Citation

  • Lin Yang & Linming Guo & Wenhai Zhang & Xiaomei Yang, 2022. "Classification of Multiple Power Quality Disturbances by Tunable-Q Wavelet Transform with Parameter Selection," Energies, MDPI, vol. 15(9), pages 1-16, May.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3428-:d:810621
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    References listed on IDEAS

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    1. Wang, Shouxiang & Chen, Haiwen, 2019. "A novel deep learning method for the classification of power quality disturbances using deep convolutional neural network," Applied Energy, Elsevier, vol. 235(C), pages 1126-1140.
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