Fault Diagnosis of Universal Circuit Breakers Based on Variational Mode Decomposition and WOA-DBN

Abstract

Universal circuit breakers are crucial devices in power systems, and the accuracy of their fault diagnosis is vital. However, existing diagnostic models suffer from low feature extraction rates and low diagnostic accuracy. In this paper, we propose a novel approach for fault diagnosis of universal circuit breakers based on analyzing vibration signals generated during the closing operation. Firstly, the vibration signal was decomposed into multiple modal components using Variable Mode Decomposition (VMD), and the modal components were subjected to time and frequency domain feature extraction. Then, the extracted features were fused and normalized to construct a training dataset for the proposed model. We propose a Deep Belief Network (DBN) diagnostic model based on the Whale Optimization Algorithm (WOA), where the WOA is employed to optimize the hyperparameters of the DBN. Experimental results demonstrate that the proposed VMD and WOA-DBN model achieved an average accuracy of 96.63%. This method enhanced the accuracy of feature extraction from vibration signals and outperformed traditional diagnostic models when using a single vibration signal for fault diagnosis of universal circuit breakers. It provides a novel solution for early fault diagnosis of universal circuit breakers.

Keywords

• variational mode decomposition
• whale optimization algorithm
• deep belief network
• universal circuit breaker fault diagnosis