Fault diagnosis of power capacitors using a convolutional neural network combined with the chaotic synchronisation method and the empirical mode decomposition method
Abstract This study combined a Convolutional Neural Network (CNN) with the chaos theory and the Empirical Mode Decomposition (EMD) method for the attenuation fault recognition of power capacitors. First, it built six capacitor analysis models, including normal capacitors, failed capacitors, and norm...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-09-01
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| Series: | IET Science, Measurement & Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/smt2.12056 |
| _version_ | 1811266906640875520 |
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| author | Shiue‐Der Lu Hong‐Wei Sian Meng‐Hui Wang Cheng‐Chien Kuo |
| author_facet | Shiue‐Der Lu Hong‐Wei Sian Meng‐Hui Wang Cheng‐Chien Kuo |
| author_sort | Shiue‐Der Lu |
| collection | DOAJ |
| description | Abstract This study combined a Convolutional Neural Network (CNN) with the chaos theory and the Empirical Mode Decomposition (EMD) method for the attenuation fault recognition of power capacitors. First, it built six capacitor analysis models, including normal capacitors, failed capacitors, and normal capacitors attenuated by 20–80%. Then a power testing machine was used for an applied voltage test of the capacitor. The EMD method was combined with the chaos synchronisation detection method to chart the discharge signals of the voltage and current that was captured by a high frequency oscilloscope into a 3D chaotic error scatter plot, as the fault diagnosis feature image. Finally, the CNN algorithm was used for the capacitor fault detection. The advantages of the proposed method are that big data are compressed to extract meaningful feature images, the operating state of the power capacitor can be detected effectively, and faults can be diagnosed according to the electrical signal change of the power capacitor. The actual measurement results showed that the accuracy of the proposed method was as high as 97% and has a high efficiency of noise rejection ability, which indicates that the method could be applied to other power‐related fields in the future. |
| first_indexed | 2024-04-12T20:52:49Z |
| format | Article |
| id | doaj.art-ae960069d61d4756b0b62c3eb6670db0 |
| institution | Directory Open Access Journal |
| issn | 1751-8822 1751-8830 |
| language | English |
| last_indexed | 2024-04-12T20:52:49Z |
| publishDate | 2021-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Science, Measurement & Technology |
| spelling | doaj.art-ae960069d61d4756b0b62c3eb6670db02022-12-22T03:17:05ZengWileyIET Science, Measurement & Technology1751-88221751-88302021-09-0115755156110.1049/smt2.12056Fault diagnosis of power capacitors using a convolutional neural network combined with the chaotic synchronisation method and the empirical mode decomposition methodShiue‐Der Lu0Hong‐Wei Sian1Meng‐Hui Wang2Cheng‐Chien Kuo3Department of Electrical Engineering National Chin‐Yi University of Technology No.57, Zhongshan Rd., Sec.2, Taiping Dist. Taichung 41170 Taiwan (R.O.C.)Department of Electrical Engineering National Taiwan University of Science and Technology No.43, Keelung Rd., Sec.4, Da'an Dist. Taipei 106335 Taiwan (R.O.C.)Department of Electrical Engineering National Chin‐Yi University of Technology No.57, Zhongshan Rd., Sec.2, Taiping Dist. Taichung 41170 Taiwan (R.O.C.)Department of Electrical Engineering National Taiwan University of Science and Technology No.43, Keelung Rd., Sec.4, Da'an Dist. Taipei 106335 Taiwan (R.O.C.)Abstract This study combined a Convolutional Neural Network (CNN) with the chaos theory and the Empirical Mode Decomposition (EMD) method for the attenuation fault recognition of power capacitors. First, it built six capacitor analysis models, including normal capacitors, failed capacitors, and normal capacitors attenuated by 20–80%. Then a power testing machine was used for an applied voltage test of the capacitor. The EMD method was combined with the chaos synchronisation detection method to chart the discharge signals of the voltage and current that was captured by a high frequency oscilloscope into a 3D chaotic error scatter plot, as the fault diagnosis feature image. Finally, the CNN algorithm was used for the capacitor fault detection. The advantages of the proposed method are that big data are compressed to extract meaningful feature images, the operating state of the power capacitor can be detected effectively, and faults can be diagnosed according to the electrical signal change of the power capacitor. The actual measurement results showed that the accuracy of the proposed method was as high as 97% and has a high efficiency of noise rejection ability, which indicates that the method could be applied to other power‐related fields in the future.https://doi.org/10.1049/smt2.12056Optical, image and video signal processingSignal processing and detectionComputer vision and image processing techniquesOther topics in statisticsOther topics in statisticsCapacitors |
| spellingShingle | Shiue‐Der Lu Hong‐Wei Sian Meng‐Hui Wang Cheng‐Chien Kuo Fault diagnosis of power capacitors using a convolutional neural network combined with the chaotic synchronisation method and the empirical mode decomposition method IET Science, Measurement & Technology Optical, image and video signal processing Signal processing and detection Computer vision and image processing techniques Other topics in statistics Other topics in statistics Capacitors |
| title | Fault diagnosis of power capacitors using a convolutional neural network combined with the chaotic synchronisation method and the empirical mode decomposition method |
| title_full | Fault diagnosis of power capacitors using a convolutional neural network combined with the chaotic synchronisation method and the empirical mode decomposition method |
| title_fullStr | Fault diagnosis of power capacitors using a convolutional neural network combined with the chaotic synchronisation method and the empirical mode decomposition method |
| title_full_unstemmed | Fault diagnosis of power capacitors using a convolutional neural network combined with the chaotic synchronisation method and the empirical mode decomposition method |
| title_short | Fault diagnosis of power capacitors using a convolutional neural network combined with the chaotic synchronisation method and the empirical mode decomposition method |
| title_sort | fault diagnosis of power capacitors using a convolutional neural network combined with the chaotic synchronisation method and the empirical mode decomposition method |
| topic | Optical, image and video signal processing Signal processing and detection Computer vision and image processing techniques Other topics in statistics Other topics in statistics Capacitors |
| url | https://doi.org/10.1049/smt2.12056 |
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