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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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10848087/ |
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| Summary: | 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. |
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| ISSN: | 2169-3536 |