用自然语言自动生成电网静态分析代码，准确率达82.38%。
Agentic Application in Power Grid Static Analysis: Automatic Code Generation and Error Correction
- 通过自然语言转MATPOWER脚本，实现电网分析自动化
- 代码保真度达82.38%，复杂任务下仍无幻觉
- 适合电力系统工程师快速构建仿真脚本
本文提出一个基于大模型的智能代理，将自然语言指令自动转换为MATPOWER脚本，用于电网静态分析。系统利用DeepSeek-OCR从MATPOWER手册构建增强向量数据库，通过三层纠错机制保障可靠性：静态预检、动态反馈回路与语义验证器。依托模型上下文协议，支持异步执行与自动调试功能。实验表明，该系统在代码保真度上达到82.38%，即使在复杂分析任务中也能有效消除幻觉现象。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。