A Hierarchical Multimodal Data Fusion Model for DC Transmission Control and Protection Logic

Conventional DC control and protection (C&P) systems rely on single-modal electrical data and are susceptible to false tripping and missed detection under complex operating conditions or novel fault types. In high-voltage direct current (HVDC) transmission, multi-modal data encompass time-series electrical quantities, unstructured transient waveforms, condition monitoring measurements, and environmental variables, each reflecting the system operating state from a distinct dimension with significant inter-modal complementarity. Nevertheless, fusing these heterogeneous modalities poses three key challenges: feature conflicts arising from data heterogeneity, difficulty embedding domain-specific C&P knowledge into data-driven models, and degraded model robustness under data noise and missing data conditions. This paper proposes a three-layer hierarchical fusion model that integrates multimodal data preprocessing, a C&P phase-aware rule-guided feature weighting strategy, and a dual-path decision mechanism. Experiments conducted on a dataset covering normal operation, typical fault, and complex operating scenarios demonstrate that the proposed model achieves an overall fault identification accuracy of 96.2%, which is 13.9 and 6.5 percentage points higher than a single-modal baseline and a generic multimodal model, respectively. The average decision latency of 7.2 ms satisfies the millisecond-level real-time requirement of industrial C&P systems, confirming the engineering applicability of the proposed approach.