Agentic Application in Power Grid Static Analysis: Automatic Code Generation and Error Correction
Qinjuan Wang, Shan Yang, Yongli Zhu
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Why It Matters
The integration of LLMs in technical domains like power grid analysis can significantly streamline workflows, reduce human error, and improve the accessibility of complex systems.
Contributions
- Introduction of an LLM agent for automatic code generation in power grid analysis and a comprehensive error-correction framework.
Insights
- The use of a three-tier error-correction system is a significant advancement in ensuring the reliability of generated code.
Limitations
- The paper may not address the scalability of the system in real-world applications or its performance across diverse power grid scenarios.
Tags
- agent
- evaluation
- llm
- other
Abstract
arXiv:2604.09995v1 Announce Type: cross 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.