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

arXiv 2604.09995 ↗First posted 11 Apr 2026eess.SY cs.AI cs.SYcited by 0

On the map: LLM agents and tool use → Financial trading and forecasting agents

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Financial trading and forecasting agents, one of 16 directions in LLM agents and tool use. Builds on 6 of its 14 references that are in the map. First posted on arXiv 11 Apr 2026.

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6 of 14 references.

References · 6

2024DeepSeek LLM: Scaling Open-Source Language Models with Longtermismcited by 621
2024Executable Code Actions Elicit Better LLM Agentscited by 537
2025DeepSeek-OCR: Contexts Optical Compressioncited by 174
2024AnyTool: Self-Reflective, Hierarchical Agents for Large-Scale API Callscited by 121
2024Enabling Large Language Models to Perform Power System Simulations with Previously Unseen Tools: A Case of Dalinecited by 6
2025Enhancing Project-Specific Code Completion by Inferring Internal API Informationcited by 5

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