Agentic Application in Power Grid Static Analysis: Automatic Code Generation and Error Correction
For power grid engineers, this automates complex static analysis tasks, reducing manual coding effort and errors.
The paper introduces an LLM agent that automates power grid static analysis by converting natural language into MATPOWER scripts, achieving 82.38% accuracy in code fidelity and eliminating hallucinations.
This paper introduces an LLM agent that automates power grid static analysis by converting natural language into MATPOWER scripts. The framework utilizes DeepSeek-OCR to build an enhanced vector database from MATPOWER manuals. To ensure reliability, it devises a three-tier error-correction system: a static pre-check, a dynamic feedback loop, and a semantic validator. Operating via the Model Context Protocol, the tool enables asynchronous execution and automatically debugging in MATLAB. Experimental results demonstrate that the system achieves a 82.38% accuracy regarding the code fidelity, effectively eliminating hallucinations even in complex analysis tasks.