What question did this study set out to answer?

This research aims to improve the assessment of circuit breaker health by addressing sample imbalance in machine learning models. The focus is on developing a more effective data augmentation method.

May 16, 2026Open Access

Multi-Stage State Assessment of Breakers Based on TCWGAN-GP and XGBoost Under Insufficient Samples

Puntos clave

This research aims to improve the assessment of circuit breaker health by addressing sample imbalance in machine learning models. The focus is on developing a more effective data augmentation method.
Proposed a Transformer-conditioned CWGAN-GP model for generating multi-class fault samples.
Developed a three-stage assessment method using XGBoost to evaluate health status, fault category, and severity sequentially.
Compared performance against traditional oversampling methods like ROS and SMOTE.
Achieved an overall assessment accuracy of 93.10%, outperforming single-stage XGBoost.
Initial anomaly detection stage requires only 0.0041 s per sample, a significant improvement over the 0.0241 s needed by the single-stage model.
TCWGAN-GP augmentation resulted in a recall rate of 91.26% and an F1-score of 92.61% on balanced datasets.

Resumen

The increasing randomness and volatility of renewable energy resources have raised higher demands for circuit breakers. Utilizing monitoring data enables more accurate condition assessment; however, the imbalance between fault and normal samples hampers the performance of machine-learning-based assessment. To address the overfitting and limited diversity of traditional oversampling methods, this paper proposes a Transformer-conditioned CWGAN-GP (TCWGAN-GP) model to generate multi-class fault samples for data augmentation. The generator of the proposed model takes random noise and class labels as input to capture the distribution characteristics of real fault samples. By combining a transformer-based generator to model inter-feature dependencies among 14 monitoring indicators and a WGAN-GP training objective with gradient penalty, the proposed approach improves training stability and synthetic sample quality. Moreover, a three-stage state assessment method based on XGBoost is developed to sequentially assess health status, fault category, and fault severity. Results demonstrate that the proposed method in the paper outperforms conventional data augmentation methods and single-stage classifiers in terms of accuracy, recall, F1-score, and online prediction efficiency. Specifically, the proposed three-stage model achieves an overall assessment accuracy of 93.10%, outperforming the single-stage XGBoost framework. In terms of online efficiency, the initial anomaly detection stage requires only 0.0041 s per sample, which is a substantial reduction compared to the 0.0241 s required by the single-stage model. Furthermore, compared to traditional Random Oversampling (ROS) and SMOTE, the TCWGAN-GP augmentation yields superior evaluation performance on fully balanced datasets, achieving a recall rate of 91.26% and an F1-score of 92.61%. Overall, the proposed TCWGAN-GP and three-stage XGBoost method contributes to addressing data imbalance challenges and improving the accuracy of circuit breaker state assessment.

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