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使用人类监督的智能体大语言模型实现可解释的灾后电网可观测性恢复
该 arXiv 论文提出一个由大语言模型编排的智能体工具调用框架，用于灾后 PMU 停运后的电网可观测性恢复。LLM 不直接求解恢复优化问题，而是协调可观测性评估、恢复规划、状态更新和操作员核验等经过验证的后端工具，并维护结构化工具调用历史与执行上下文，使恢复决策可追溯、可解释，并支持操作员在恢复过程中的上下文问答。在 IEEE 30 节点和 57 节点系统上的仿真显示，该框架的可观测性恢复效果与混合整数线性规划（MILP）方案相当，同时提供基于工具的解释、交互式操作支持和人类监督执行。
AI推荐理由 arXiv 预印本，作者与摘要明确，方法描述具体：LLM 不直接求解恢复优化问题，而是编排可验证的后端工具（可观测性评估、恢复规划、状态更新、操作员核验），并保留结构化工具调用历史以支持可追溯与可解释性；在 IEEE 30 与 57 节点系统上仿真，称可观测性恢复效果与 MILP 相当。新信息在于将 agentic tool-calling 与人类监督引入灾后 PMU 恢复这一具体电力场景，并提供工具支撑的解释与交互式问答。局限：仅为仿真结果，无真实电网部署或现场验证，也未给出与 MILP 的量化差距细节；摘要未提供代码或数据。
72
正文
Electrical Engineering and Systems Science > Systems and Control
arXiv:2609.16774 (eess)
[Submitted on 15 Sep 2026]
Title:Explainable Post-Disaster Grid Observability Recovery Using Human-Oversight Agentic LLMs
Authors:Biswas Rudra Jyoti Arka, Sadman Sakib, Md. Zahidul Islam, Shamsun Nahar Edib
View a PDF of the paper titled Explainable Post-Disaster Grid Observability Recovery Using Human-Oversight Agentic LLMs, by Biswas Rudra Jyoti Arka and 3 other authors
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Abstract:Post-disaster phasor measurement unit (PMU) outages reduce power-system observability and degrade operator situational awareness, requiring sequential restoration under limited resources. Existing PMU restoration methods based on optimization or heuristics can generate restoration schedules, but they often provide limited support for explanation, traceability, and operator interaction. This paper proposes an agentic tool-calling framework orchestrated by a large language model (LLM) for post-disaster PMU restoration and grid observability recovery. In this framework, the LLM does not directly solve the restoration optimization problem; instead, it coordinates validated backend tools required for post-disaster restoration, including observability assessment, restoration planning, state updates, and operator verification. The framework also maintains a structured tool-call history and execution context that keep restoration decisions traceable and explainable, while enabling context-aware operator question answering during the restoration process. Simulation results on IEEE 30-bus and IEEE 57-bus systems show that the proposed framework achieves observability recovery comparable to a mixed-integer linear programming (MILP) solution, while providing tool-grounded explanations, interactive operator support, and human-overseen execution.
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Systems and Control (eess.SY)
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arXiv:2609.16774 [eess.SY]
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arXiv:2609.16774v1 [eess.SY] for this version)
https://doi.org/10.48550/arXiv.2609.16774
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From: Md Zahidul Islam [view email]
[v1]
Tue, 15 Sep 2026 07:43:32 UTC (501 KB)
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