InvMOE: MOEs Based Invariant Representation Learning for Fault Detection in Converter Stations

Converter stations are pivotal in high-voltage direct current (HVDC) systems, enabling power conversion between an alternating current (AC) and a direct current (DC) while ensuring efficient and stable energy transmission. Fault detection in converter stations is crucial for maintaining their reliability and operational safety. This paper focuses on image-based detection of five common faults: metal corrosion, discoloration of desiccant in breathers, insulator breakage, hanging foreign objects, and valve cooling water leakage. Despite advancements in deep learning, existing detection methods face two major challenges: limited model generalization due to diverse and complex backgrounds in converter station environments and sparse supervision signals caused by the high cost of collecting labeled images for certain faults. To overcome these issues, we propose InvMOE, a novel fault detection algorithm with two core components: (1) invariant representation learning, which captures task-relevant features and mitigates background noise interference, and (2) multi-task training using a mixture of experts (MOE) framework to adaptively optimize feature learning across tasks and address label sparsity. Experimental results on real-world datasets demonstrate that InvMOE achieves superior generalization performance and significantly improves detection accuracy for tasks with limited samples, such as valve cooling water leakage. This work provides a robust and scalable approach for enhancing fault detection in converter stations.