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Fault Diagnosis in Power Grids with Large Language Model

arXiv 2407.08836 ↗First posted 11 Jul 2024cs.CL cs.AIcited by 0

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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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 21 references that are in the map. First posted on arXiv 11 Jul 2024.

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19 of 21 references.

References · 19

  1. 2023A Comprehensive Overview of Large Language Modelscited by 639
  2. 2023QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Modelscited by 154
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  10. 2024MemoryMamba: Memory-Augmented State Space Model for Defect Recognitioncited by 24
  11. 2018Real-time Faulted Line Localization and PMU Placement in Power Systems through Convolutional Neural Networkscited by 14
  12. 2023ChatGPT and Other Large Language Models for Cybersecurity of Smart Grid Applicationscited by 13
7 more
  1. 2023Applying Large Language Models to Power Systems: Potential Security Threatscited by 8
  2. 2022Data-driven design of fault diagnosis for three-phase PWM rectifier using random forests technique with transient synthetic featurescited by 3
  3. 2023A Heterogeneous Graph-Based Multi-Task Learning for Fault Event Diagnosis in Smart Gridcited by 3
  4. 2022Integrated Fault Diagnosis and Control Design for DER Inverters using Machine Learning Methodscited by 2
  5. 2022Review for AI-based Open-Circuit Faults Diagnosis Methods in Power Electronics Converterscited by 2
  6. 2022Optimizing a Digital Twin for Fault Diagnosis in Grid Connected Inverters -- A Bayesian Approachcited by 2
  7. 2022Autonomous Smart Grid Fault Detectioncited by 1

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