LAPSE:2023.12597
Published Article
LAPSE:2023.12597
HOG-SVM-Based Image Feature Classification Method for Sound Recognition of Power Equipments
February 28, 2023
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.
Record ID
Keywords
electric power equipment, HOG feature extraction, image processing, SVM classifier, voice recognition
Subject
Suggested Citation
Bai K, Zhou Y, Cui Z, Bao W, Zhang N, Zhai Y. HOG-SVM-Based Image Feature Classification Method for Sound Recognition of Power Equipments. (2023). LAPSE:2023.12597
Author Affiliations
Bai K: Department of Automation, North China Electric Power University, Baoding 071003, China
Zhou Y: SPIC Central Research Institute, Beijing 102209, China
Cui Z: Department of Automation, North China Electric Power University, Baoding 071003, China
Bao W: SPIC Central Research Institute, Beijing 102209, China
Zhang N: SPIC Central Research Institute, Beijing 102209, China
Zhai Y: Department of Automation, North China Electric Power University, Baoding 071003, China [ORCID]
Zhou Y: SPIC Central Research Institute, Beijing 102209, China
Cui Z: Department of Automation, North China Electric Power University, Baoding 071003, China
Bao W: SPIC Central Research Institute, Beijing 102209, China
Zhang N: SPIC Central Research Institute, Beijing 102209, China
Zhai Y: Department of Automation, North China Electric Power University, Baoding 071003, China [ORCID]
Journal Name
Energies
Volume
15
Issue
12
First Page
4449
Year
2022
Publication Date
2022-06-18
Published Version
ISSN
1996-1073
Version Comments
Original Submission
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PII: en15124449, Publication Type: Journal Article
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LAPSE:2023.12597
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doi:10.3390/en15124449
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[v1] (Original Submission)
Feb 28, 2023
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Feb 28, 2023
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