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HOG-SVM-Based Image Feature Classification Method for Sound Recognition of Power Equipments

Author

Listed:
  • Kang Bai

    (Department of Automation, North China Electric Power University, Baoding 071003, China)

  • Yong Zhou

    (SPIC Central Research Institute, Beijing 102209, China)

  • Zhibo Cui

    (Department of Automation, North China Electric Power University, Baoding 071003, China)

  • Weiwei Bao

    (SPIC Central Research Institute, Beijing 102209, China)

  • Nan Zhang

    (SPIC Central Research Institute, Beijing 102209, China)

  • Yongjie Zhai

    (Department of Automation, North China Electric Power University, Baoding 071003, China)

Abstract

In this paper, a method of power system equipment recognition based on image processing is proposed. Firstly, we carry out wavelet transform on the sound signal of power system equipment collected from the site, and obtain the wavelet coefficient–time diagram. Then, the similarity of wavelet coefficients–time images of different equipment and the same equipment in different periods is calculated, which is used as the basis of the feasibility of image recognition. Finally, we select the HOG features of the image, and classify the selected features using SVM classifier. The method proposed in this paper can accurately identify and classify power system equipment through sound signals, and is different from the traditional method of classifying sound signals directly. The advantages of image processing can be effectively utilized through image processing to avoid the limitations of sound signal processing.

Suggested Citation

  • Kang Bai & Yong Zhou & Zhibo Cui & Weiwei Bao & Nan Zhang & Yongjie Zhai, 2022. "HOG-SVM-Based Image Feature Classification Method for Sound Recognition of Power Equipments," Energies, MDPI, vol. 15(12), pages 1-12, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:12:p:4449-:d:842181
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    References listed on IDEAS

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    1. Natalia Bakhtadze & Igor Yadikin, 2021. "Analysis and Prediction of Electric Power System’s Stability Based on Virtual State Estimators," Mathematics, MDPI, vol. 9(24), pages 1-16, December.
    2. Song, Zhe & Zhang, Zijun & Jiang, Yu & Zhu, Jin, 2018. "Wind turbine health state monitoring based on a Bayesian data-driven approach," Renewable Energy, Elsevier, vol. 125(C), pages 172-181.
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