GridMind：面向电力系统分析与运行的大语言模型驱动智能体
GridMind: LLMs-Powered Agents for Power System Analysis and Operations
AI总结：
针对传统电力系统分析工作流复杂、决策效率低的问题，提出融合大语言模型与工程求解器的多智能体系统GridMind，通过自然语言协调专业分析且保证精度，实验验证其在IEEE算例上稳定有效，小模型也能兼顾精度与速度，为工程科学计算提供新范式。
摘要：
传统电力系统分析工作流的复杂性，为现代电网的高效决策带来了显著障碍。本文提出GridMind，这是一套将大语言模型（LLMs）与确定性工程求解器相结合的多智能体AI系统，可实现电力系统分析的对话式科学计算。该系统采用多类专用智能体，通过自然语言界面协调交流最优潮流（AC Optimal Power Flow）与N-1 contingency分析，同时通过函数调用保证数值精度。GridMind解决了工作流集成、知识可及性、上下文保留以及专家决策支持增强等问题。在IEEE测试算例上的实验评估表明，所提出的智能体框架在所有测试的语言模型中均能持续输出正确解，规模更小的LLMs可达到相当的分析精度，同时计算延迟更低。本研究确立了智能体AI作为科学计算可行范式的地位，展示了对话界面如何在保障关键工程应用所需数值严谨性的同时，提升可及性。
英文摘要：
The complexity of traditional power system analysis workflows presents significant barriers to efficient decision-making in modern electric grids. This paper presents GridMind, a multi-agent AI system that integrates Large Language Models (LLMs) with deterministic engineering solvers to enable conversational scientific computing for power system analysis. The system employs specialized agents coordinating AC Optimal Power Flow and N-1 contingency analysis through natural language interfaces while maintaining numerical precision via function calls. GridMind addresses workflow integration, knowledge accessibility, context preservation, and expert decision-support augmentation. Experimental evaluation on IEEE test cases demonstrates that the proposed agentic framework consistently delivers correct solutions across all tested language models, with smaller LLMs achieving comparable analytical accuracy with reduced computational latency. This work establishes agentic AI as a viable paradigm for scientific computing, demonstrating how conversational interfaces can enhance accessibility while preserving numerical rigor essential for critical engineering applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
