# Fault Diagnosis in Power Grids with Large Language Model

arXiv: https://arxiv.org/abs/2407.08836 · HTML: https://mlsanity.com/paper/2407.08836/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 21 references that are in the map.
- First posted on arXiv 11 Jul 2024.

## Builds on (most cited first, 19 of 19)

- [A Comprehensive Overview of Large Language Models](https://mlsanity.com/paper/2307.06435/) (2023)
- [QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2309.14717/) (2023)
- [Visual In-Context Learning for Large Vision-Language Models](https://mlsanity.com/paper/2402.11574/) (2024)
- [Large Language Models for Robotics: Opportunities, Challenges, and Perspectives](https://mlsanity.com/paper/2401.04334/) (2024)
- [Lean Copilot: Large Language Models as Copilots for Theorem Proving in Lean](https://mlsanity.com/paper/2404.12534/) (2024)
- [ClarET: Pre-training a Correlation-Aware Context-To-Event Transformer for Event-Centric Generation and Classification](https://mlsanity.com/paper/2203.02225/) (2022)
- [EventBERT: A Pre-Trained Model for Event Correlation Reasoning](https://mlsanity.com/paper/2110.06533/) (2021)
- [Large Language Models for Scientific Synthesis, Inference and Explanation](https://mlsanity.com/paper/2310.07984/) (2023)
- [The Shaky Foundations of Clinical Foundation Models: A Survey of Large Language Models and Foundation Models for EMRs](https://mlsanity.com/paper/2303.12961/) (2023)
- [MemoryMamba: Memory-Augmented State Space Model for Defect Recognition](https://mlsanity.com/paper/2405.03673/) (2024)
- [Real-time Faulted Line Localization and PMU Placement in Power Systems through Convolutional Neural Networks](https://mlsanity.com/paper/1810.05247/) (2018)
- [ChatGPT and Other Large Language Models for Cybersecurity of Smart Grid Applications](https://mlsanity.com/paper/2311.05462/) (2023)
- [Applying Large Language Models to Power Systems: Potential Security Threats](https://mlsanity.com/paper/2311.13361/) (2023)
- [Data-driven design of fault diagnosis for three-phase PWM rectifier using random forests technique with transient synthetic features](https://mlsanity.com/paper/2211.02631/) (2022)
- [A Heterogeneous Graph-Based Multi-Task Learning for Fault Event Diagnosis in Smart Grid](https://mlsanity.com/paper/2309.09921/) (2023)
- [Integrated Fault Diagnosis and Control Design for DER Inverters using Machine Learning Methods](https://mlsanity.com/paper/2202.09996/) (2022)
- [Review for AI-based Open-Circuit Faults Diagnosis Methods in Power Electronics Converters](https://mlsanity.com/paper/2209.14058/) (2022)
- [Optimizing a Digital Twin for Fault Diagnosis in Grid Connected Inverters -- A Bayesian Approach](https://mlsanity.com/paper/2212.03564/) (2022)
- [Autonomous Smart Grid Fault Detection](https://mlsanity.com/paper/2206.14150/) (2022)

## Built on by (most cited first, 1 of 1)

- [Training Numerical Intelligence via Auto-Diagnosis and Skill Discovery](https://mlsanity.com/paper/2610.03872/) (2026)

## Abstract (arXiv)

Power grid fault diagnosis is a critical task for ensuring the reliability and stability of electrical infrastructure. Traditional diagnostic systems often struggle with the complexity and variability of power grid data. This paper proposes a novel approach that leverages Large Language Models (LLMs), specifically ChatGPT and GPT-4, combined with advanced prompt engineering to enhance fault diagnosis accuracy and explainability. We designed comprehensive, context-aware prompts to guide the LLMs in interpreting complex data and providing detailed, actionable insights. Our method was evaluated against baseline techniques, including standard prompting, Chain-of-Thought (CoT), and Tree-of-Thought (ToT) methods, using a newly constructed dataset comprising real-time sensor data, historical fault records, and component descriptions. Experimental results demonstrate significant improvements in diagnostic accuracy, explainability quality, response coherence, and contextual understanding, underscoring the effectiveness of our approach. These findings suggest that prompt-engineered LLMs offer a promising solution for robust and reliable power grid fault diagnosis.