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| Circuit Breaker Fault Diagnosis Method Based on IWGAN-GP Model under Imbalanced Data |
| Wang Zhenyuan1, Chen Lei1, Wan Shuting2, Hu Yuyao1, Jiang Ningbo1 |
1. School of Electrical Engineering Shandong University of Technology Zibo 255000 China;
2. Hebei Key Laboratory of Electric Machinery Health Maintenance & Failure Prevention North China Electric Power University Baoding 071003 China |
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Abstract Accurate and reliable fault diagnosis of high-voltage circuit breakers is critical to ensuring the safety and stability of modern power systems. However, in practical applications, circuit breakers operate under stable conditions for long periods, and actual fault events occur infrequently. As a result, the available fault samples are scarce and highly imbalanced compared to normal-operation data, significantly degrading the performance of data-driven diagnostic models. Conventional resampling strategies, such as oversampling and undersampling, have been used to mitigate imbalance. However, these strategies often lead to overfitting, noise amplification, or the loss of essential information. Generative Adversarial Networks (GANs) have emerged as powerful tools for data augmentation. However, standard GANs suffer from training instability, mode collapse, and limited feature learning capacity when applied to complex vibration signals. Therefore, this paper proposes an improved Wasserstein GAN with gradient penalty (IWGAN-GP) framework, integrated with a convolutional neural network (CNN).
In the proposed method, vibration signals collected from a 35 kV CT14 spring-operated circuit breaker are preprocessed using pre-emphasis, windowing, framing, and Fourier transformation. Mel-frequency cepstral coefficients (MFCC) are then extracted to capture both frequency and temporal characteristics of the signals and serve as input to the IWGAN-GP model, which employs CNN-based generator and discriminator architectures. The generator uses transposed convolutional layers to reconstruct high-resolution feature maps, while the discriminator applies convolutional operations to extract local patterns and distinguish between real and generated data. A gradient penalty term is introduced to ensure the discriminator's Lipschitz continuity and eliminate conventional WGANs’ instability and weight-clipping limitations.
Tests were conducted using six operating states, including normal conditions and five common fault scenarios: loose base screws, buffer spring fatigue, transmission mechanism fault, closing spring fatigue, and opening spring fatigue. For each fault type, 30 groups of vibration data were collected, while 60 groups were collected for the normal state. The IWGAN-GP model was trained to generate balanced fault datasets, which were then compared with data generated by SMOTE, conventional GANs, and WGAN-GP. Quantitative evaluations using the Inception Score (IS), Fréchet Inception Distance (FID), and Kernel Inception Distance (KID) showed that IWGAN-GP achieved the best balance between diversity and fidelity, with IS of 9.01, FID of 12.96, and KID of 0.69. Visual inspection of the generated MFCC feature maps confirmed that the IWGAN-GP model successfully captured both global patterns and subtle local variations in vibration signals.
To validate diagnostic performance, the augmented datasets were used to train CNN classifiers optimized through Bayesian hyperparameter tuning. The model trained on IWGAN-GP data achieved an overall accuracy of 98%, significantly higher than those trained on original imbalanced data, SMOTE, GAN, and WGAN-GP. Moreover, IWGAN-GP training exhibited faster convergence, lower loss, and improved robustness to small- sample limitations.
In conclusion, this research establishes IWGAN-GP for fault diagnosis under imbalanced data conditions. By integrating CNN feature extraction with gradient penalty stabilization, the method overcomes the limitations of traditional GAN-based augmentation and enables the generation of high-quality synthetic samples. The results confirm that IWGAN-GP significantly enhances classification accuracy, accelerates training convergence, and improves generalization performance in diagnosing multiple fault states of circuit breakers. These findings offer a promising pathway to advance intelligent monitoring and predictive maintenance for high-voltage power equipment.
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Received: 17 June 2025
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2026, Vol. 41 
