MSRDSN: A Novel Deep Learning Model for Fault Diagnosis of High-Voltage Disconnectors

The operational state of high-voltage disconnectors plays a critical role in ensuring the safety, stability, and power supply reliability of electrical systems. To enable accurate identification of the operational status of high-voltage disconnectors, this paper proposes a fault diagnosis method based on a Multi-Scale Residual Depthwise Separable Convolutional Neural Network (MSRDSN). First, wavelet transform is applied to vibration signals to perform multi-scale analysis and enhance detail resolution. Then, a novel network architecture, referred to as RDSN, is constructed to extract discriminative high-level features from vibration signals by integrating residual learning blocks and depthwise separable convolution blocks. Furthermore, a combined loss function is introduced to optimize the RDSN, which simultaneously maximizes inter-class distance, minimizes intra-class distance, and reduces feature redundancy. Experimental results show that the proposed method achieves a top accuracy of 99.44% on a balanced dataset, outperforming the sub-optimal approach by 1.11%. This study offers a novel and effective solution for fault diagnosis in high-voltage disconnectors.