  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.
