The integration of experiment technologies with large language models (LLMs) is transforming scientific research, offering AI capabilities beyond specialized problem-solving to becoming research assistants for human scientists. In power systems, simulations are essential for research. However, LLMs face significant challenges in power system simulations due to limited pre-existing knowledge and the complexity of power grids. To address this issue, this work proposes a modular framework that integrates expertise from both the power system and LLM domains. This framework enhances LLMs' ability to perform power system simulations on previously unseen tools. Validated using 34 simulation tasks in Daline, a (optimal) power flow simulation and linearization toolbox not yet exposed to LLMs, the proposed framework improved GPT-4o's simulation coding accuracy from 0% to 96.07%, also outperforming the ChatGPT-4o web interface's 33.8% accuracy (with the entire knowledge base uploaded). These results highlight the potential of LLMs as research assistants in power systems.

翻译：实验技术与大型语言模型（LLMs）的融合正在变革科学研究，使人工智能不仅能够解决特定问题，更能成为人类科学家的研究助手。在电力系统中，仿真是研究工作的核心环节。然而，由于LLMs在电力系统领域先验知识有限，且电网结构复杂，其在电力系统仿真中面临重大挑战。为解决这一问题，本研究提出一个融合电力系统与LLM领域专业知识的模块化框架。该框架增强了LLMs利用先前未见工具进行电力系统仿真的能力。通过在Daline——一个尚未对LLMs公开的（最优）潮流仿真与线性化工具箱——中执行34项仿真任务进行验证，所提框架将GPT-4o的仿真代码生成准确率从0%提升至96.07%，同时显著优于ChatGPT-4o网页界面（在完整知识库上传条件下）33.8%的准确率。这些结果凸显了LLMs作为电力系统研究助手的巨大潜力。