Fault Diagnosis of Magnetically Controlled On-Column Circuit Breaker Based on Small Sample Condition
As the pivotal component of power grid supply protection, the fault diagnosis of magnetically controlled on-column circuit breakers is a crucial element in ensuring regional power supply reliability. Addressing challenges such as difficult fault signal acquisition, noise interference, and a limited...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10848087/ |
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| author | He Tian Chao Liang Wenpeng Ma Tianchang Zhang |
| author_facet | He Tian Chao Liang Wenpeng Ma Tianchang Zhang |
| author_sort | He Tian |
| collection | DOAJ |
| description | As the pivotal component of power grid supply protection, the fault diagnosis of magnetically controlled on-column circuit breakers is a crucial element in ensuring regional power supply reliability. Addressing challenges such as difficult fault signal acquisition, noise interference, and a limited number of fault samples, this paper introduces a fault diagnosis method for magnetically controlled on-column circuit breakers under conditions of small sample sizes, based on VAE-ACGAN-SDAE. Initially, a Variational Autoencoder (VAE) is employed to extract the latent distribution of genuine samples, which are then integrated with the Auxiliary Classifier Generative Adversarial Network (ACGAN) generator to learn the characteristics of real data. Subsequently, to address the problem of real-world operational data being susceptible to noise, a Stacked Denoising Autoencoder (SDAE) is utilized as the discriminator in the ACGAN framework. This approach not only enhances noise resistance and optimizes the feature centers but also synchronizes training with the VAE, thereby improving the quality of the generated samples and refining the weight bias parameters of the discriminator. The experimental outcomes demonstrate that the generated data can be systematically produced and categorized. Optimal sample classification is achieved when the expansion ratio is set to 3, and the method achieves the highest accuracy of 98.8% with a minimal number of fault samples. Compared to the VAE-GAN-CNN network, this network shows a 3.6% increase in accuracy. |
| format | Article |
| id | doaj-art-8635e7e9a73f4affa48f630b62cbc1fb |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-8635e7e9a73f4affa48f630b62cbc1fb2025-01-31T00:01:07ZengIEEEIEEE Access2169-35362025-01-0113184571846910.1109/ACCESS.2025.353246910848087Fault Diagnosis of Magnetically Controlled On-Column Circuit Breaker Based on Small Sample ConditionHe Tian0https://orcid.org/0000-0002-6718-2000Chao Liang1https://orcid.org/0009-0000-0159-9949Wenpeng Ma2https://orcid.org/0000-0002-9714-2544Tianchang Zhang3Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control, School of Mechanical Engineering, Tianjin University of Technology, Tianjin, ChinaTianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control, School of Mechanical Engineering, Tianjin University of Technology, Tianjin, ChinaTianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control, School of Mechanical Engineering, Tianjin University of Technology, Tianjin, ChinaTianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control, School of Mechanical Engineering, Tianjin University of Technology, Tianjin, ChinaAs the pivotal component of power grid supply protection, the fault diagnosis of magnetically controlled on-column circuit breakers is a crucial element in ensuring regional power supply reliability. Addressing challenges such as difficult fault signal acquisition, noise interference, and a limited number of fault samples, this paper introduces a fault diagnosis method for magnetically controlled on-column circuit breakers under conditions of small sample sizes, based on VAE-ACGAN-SDAE. Initially, a Variational Autoencoder (VAE) is employed to extract the latent distribution of genuine samples, which are then integrated with the Auxiliary Classifier Generative Adversarial Network (ACGAN) generator to learn the characteristics of real data. Subsequently, to address the problem of real-world operational data being susceptible to noise, a Stacked Denoising Autoencoder (SDAE) is utilized as the discriminator in the ACGAN framework. This approach not only enhances noise resistance and optimizes the feature centers but also synchronizes training with the VAE, thereby improving the quality of the generated samples and refining the weight bias parameters of the discriminator. The experimental outcomes demonstrate that the generated data can be systematically produced and categorized. Optimal sample classification is achieved when the expansion ratio is set to 3, and the method achieves the highest accuracy of 98.8% with a minimal number of fault samples. Compared to the VAE-GAN-CNN network, this network shows a 3.6% increase in accuracy.https://ieeexplore.ieee.org/document/10848087/Fault diagnosisgenerated datamagnetically controlled on-column circuit breakersmall sampleVAE-ACGAN-SDAE |
| spellingShingle | He Tian Chao Liang Wenpeng Ma Tianchang Zhang Fault Diagnosis of Magnetically Controlled On-Column Circuit Breaker Based on Small Sample Condition IEEE Access Fault diagnosis generated data magnetically controlled on-column circuit breaker small sample VAE-ACGAN-SDAE |
| title | Fault Diagnosis of Magnetically Controlled On-Column Circuit Breaker Based on Small Sample Condition |
| title_full | Fault Diagnosis of Magnetically Controlled On-Column Circuit Breaker Based on Small Sample Condition |
| title_fullStr | Fault Diagnosis of Magnetically Controlled On-Column Circuit Breaker Based on Small Sample Condition |
| title_full_unstemmed | Fault Diagnosis of Magnetically Controlled On-Column Circuit Breaker Based on Small Sample Condition |
| title_short | Fault Diagnosis of Magnetically Controlled On-Column Circuit Breaker Based on Small Sample Condition |
| title_sort | fault diagnosis of magnetically controlled on column circuit breaker based on small sample condition |
| topic | Fault diagnosis generated data magnetically controlled on-column circuit breaker small sample VAE-ACGAN-SDAE |
| url | https://ieeexplore.ieee.org/document/10848087/ |
| work_keys_str_mv | AT hetian faultdiagnosisofmagneticallycontrolledoncolumncircuitbreakerbasedonsmallsamplecondition AT chaoliang faultdiagnosisofmagneticallycontrolledoncolumncircuitbreakerbasedonsmallsamplecondition AT wenpengma faultdiagnosisofmagneticallycontrolledoncolumncircuitbreakerbasedonsmallsamplecondition AT tianchangzhang faultdiagnosisofmagneticallycontrolledoncolumncircuitbreakerbasedonsmallsamplecondition |