Control-room application requires a language model to read operation instructions, match terms with buses, branches, generators and ratings; return the action that remains feasible according to AC power flow equations. In short, such general large-language models are good at producing fluent Dispatching or contingency Explanation but prone to recommending set-Point changes that break the dynamic equilibrium of active and passive power balance, exceeding branches’ thermal Limits, or citing rules unrelated to the Current Topology. Based on the integration of the five audit components in this paper’s modular power sector large model: domain knowledge router, topology-aware graph adapter, physics-constraint verification layer, solver bridge and response repair module. Benchmarking links to public transmission case, PGLib-style AC-OPF case, GEFCom-derived load and renewable profiles; solver trace; operation rule fragment; equipment description; task-specific question-and-answer pair. Dispatch reasoning with feasibility repair, state-estimation explanation, and contingency diagnosis are evaluated against a general LLM, retrieval-augmented LLM, supervised fine-tuned LLM, graph-adapted LLM, and physics-checked variant. The full architecture reached a 91.7% feasible-answer rate, a 1.43% mean OPF cost gap, a 0.0042 p.u. normalized power-flow residual, a 0.0069 p.u. voltage-magnitude MAE, and 89.1% macro-F1 for contingency diagnosis. Ablation results assign different responsibilities to each module: Physical verification eliminates most of the infeasible Responses; The graph adapter provides a larger boost when Topology is perturbed. Finally, Sensitivity Analysis has shown a relationship with the calibration between Knowledge Coverage and Physical loss-weighting is over-estimated to slow down repairs And text content incompleteness. This kind of result is in agreement with the large model of language generation, network representation, solver-and-communicator, and physical test mentioned above from the power industry.
