Intelligent Fault Diagnosis for HVDC Systems Based on Knowledge Graph and Pre-Trained Models: A Critical and Comprehensive Review

High-voltage direct-current (HVDC) systems are essential for large-scale renewable integration and asynchronous interconnection, yet their complex topologies and multi-type faults expose the limits of threshold- and signal-based diagnostics. These methods degrade under noisy, heterogeneous measurements acquired under dynamic operating conditions, resulting in poor adaptability, reduced accuracy, and high latency. To overcome these shortcomings, the synergistic use of knowledge graphs (KGs) and pre-trained models (PTMs) is emerging as a next-generation paradigm. KGs encode equipment parameters, protection logic, and fault propagation paths in an explicit, human-readable structure, while PTMs provide transferable representations that remain effective under label scarcity and data diversity. Coupled within a perception–cognition–decision loop, PTMs first extract latent fault signatures from multi-modal records; KGs then enable interpretable causal inference, yielding both precise localization and transparent explanations. This work systematically reviews the theoretical foundations, fusion strategies, and implementation pipelines of KG-PTM frameworks tailored to HVDC systems, benchmarking them against traditional diagnostic schemes. The paradigm demonstrates superior noise robustness, few-shot generalization, and decision explainability. However, open challenges remain, such as automated, conflict-free knowledge updating; principled integration of electro-magnetic physical constraints; real-time, resource-constrained deployment; and quantifiable trustworthiness. Future research should therefore advance autonomous knowledge engineering, physics-informed pre-training, lightweight model compression, and standardized evaluation platforms to translate KG-PTM prototypes into dependable industrial tools for intelligent HVDC operation and maintenance.