Diagnosis of Power System Defects by Large Language Models and Graph Neural Networks

Defect ratings and analysis and processing of different devices and equipment in the power system are often affected by the subjectivity of operation and maintenance personnel, resulting in different severity ratings for the same defect text description. Differences in expertise also lead to differences in diagnostic analysis and different diagnostic efficiency. In order to improve the accuracy and efficiency of defect diagnosis, a defect text rating classification method based on graph neural network and a large model intelligent diagnosis and analysis assistant are proposed. Firstly, a professional dictionary is constructed to normalize the text description using natural language processing algorithms. Secondly, the semantic representation of defective text is optimized by statistical methods. Then, graph attention neural network and robustly optimized BERT approach (RoBERTa) are integrated to accurately rate and classify defective text. Finally, low-rank adaptation (LoRA) fine-tuning training based on the large language model Qwen1.5-14B-Chat is performed to obtain the large model Qwen-ElecDiag for power equipment diagnosis, which is combined with retrieval enhancement to generate the assistant for defect diagnosis of technology development equipment. In addition, the collation provides the instruction dataset for fine-tuning the power equipment diagnosis macromodel. Comparative experimental results show that the proposed graph neural network-based defect rating classification method improves nearly 8 percentage points in accuracy over the optimal baseline model BERT; the diagnostic assistant??s power knowledge as well as defect diagnostic capability is improved. By improving the accuracy of defect ratings and providing comprehensive specialized diagnostic suggestions, it not only improves the intelligent level of power equipment O&M, but also provides new solutions for intelligent O&M in other vertical fields.