Robust fault diagnosis of a high-voltage circuit breaker via an ensemble echo state network with evidence fusion

Reliable mechanical fault diagnosis of high-voltage circuit breakers is important to ensure the safety of electric power systems. Recent fault diagnosis approaches are mostly based on a single classifier whose performance relies heavily on expert prior knowledge. In this study, we propose an improved Dempster–Shafer evidence theory fused echo state neural network, an ensemble classifier for fault diagnosis. Evidence credibility is calculated through the evidence deviation matrix and the segmented circle function and employed as credibility weights to rectify the raw evidence. Then, an improved Dempster–Shafer evidence fusion algorithm is proposed to fuse evidence from different echo state network modules and sensors. Unlike conventional classifiers, the proposed methodology consists of multiple echo state neural network modules. It has better flexibility and stronger robustness, and its model performance is not sensitive to network parameters. Comparative analysis indicates that it can handle the paradox evidence fusion analysis and thus can achieve better diagnostic performance. The superiority of the reported fault diagnosis approaches is verified with the experimental data of a ZN12 high-voltage circuit breaker.