Agentic Application in Power Grid Static Analysis: Automatic Code Generation and Error Correction

TL;DR

This paper presents an LLM-based system that automates power grid analysis by generating and debugging MATLAB code from natural language, achieving high accuracy and reliability.

Contribution

It introduces a novel framework combining LLMs, OCR, and multi-tier error correction for automated power grid static analysis.

Findings

Achieves 82.38% code fidelity accuracy.

Effectively eliminates hallucinations in code generation.

Enables asynchronous debugging and validation in MATLAB.

Abstract

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.