Abstract
Due to long-term outdoor working, High-Voltage Disconnectors (HVDs) are prone to potential faults. Currently, most studies on HVD state diagnosis methods have tested only one type of HVD, and the generalization capability of these methods for other HVDs has not been verified. In this paper, we propose an HVD state diagnosis method featuring adaptive recognition capabilities based on Fault Difference Signals, Adjusted Relative Position Matrix and Convolutional Neural Networks (FDS-ARPM-CNN). First, we align the measured operational power signal of the HVD drive motor with the recorded normal operational power signal, deriving the FDS through subtraction. Next, to address the issue of traditional Relative Position Matrix (RPM) conversion processes that lose sample amplitude information, we introduce a targeted improvement to the relative position matrix calculation method, converting the one-dimensional FDS into a two-dimensional image. Finally, we achieve high-accuracy diagnosis and classification of HVD states using a CNN that incorporates Batch Normalization (BN) and GELU activation functions. Experimental validation demonstrates that the neural network model, trained on one model of HVD, maintains strong generalization capabilities on data from other HVD models. This method effectively alleviates the challenges of acquiring fault samples in data-driven approaches for HVD state diagnosis, showcasing significant practical value.