Control Mode Switching-Enabled Physics-Guided Multiagent Graph Learning for Real-Time AC/DC Power Flow

Existing ac/dc power flow computations necessitate sequential convergence-oriented trial-and-error under various dc control modes, rising computational burden. This article thus proposes a physics-guided multiagent graph learning (PG-MAGL) method toward real-time power flow analysis with dc control mode adaptation. The tailored graph structure with built-in dc control modes and state variables is first advanced to ensure topology adaptability. Then, MAGL is proposed to enable adaptive jump over dc control modes. The trick is to organize multiagents to parameterize power flow solutions under various dc control modes and set aside trigger signals according to the operational violations of converters for the agent switching to the follow-up agent. To clarify the trigger signals, an augmented Lagrangian method-based PG-MAGL method is finally designed. It relaxes the control boundaries into the violation minimizers and enforces other constraints, such that dc control switching can be identified by the only violation signal. Utilizing inductive biases to rectify experiential biases in pure data-driven models, PG-MAGL enables precise inference of dc control mode feasibility. Case study shows that, relative to the other seven data-driven rivals, only the proposed method matches the performance of the model-based baseline, also beats it in efficiency beyond ten times.