电力建设2025，Vol.46Issue(10)：44-57,14.DOI:10.12204/j.issn.1000-7229.2025.10.005
基于专家知识与大语言模型混合增强的水电站告警智能诊断
Intelligent Diagnosis of Hydroelectric Power Station Alarm Based on Hybrid-Augmentation of Expert Knowledge and Large Language Model
摘要
Abstract
[Objective]To address the problems of large sample imbalance and low diagnostic efficiency in traditional hydropower station monitoring and alarm event diagnosis,as well as the lack of interpretability and difficulty in ensuring diagnostic accuracy in artificial intelligence-based diagnostic methods,a deep learning framework based on a large language model(LLM)for hydropower station alarm event sample enhancement and fault diagnosis is designed.Moreover,regular expressions based on expert knowledge are introduced to verify the alarm diagnosis results and improve their credibility.[Methods]First,considering the issue of imbalanced monitoring of event samples in a hydropower station,the SimBERT model is used to increase the amount of data for types with fewer samples.The enhanced data are then mixed with the original data to form the input samples of the deep learning network.Second,based on power grid monitoring alarm rules,regular expressions for typical faults are constructed to achieve accurate discrimination of typical faults.Finally,based on the ERNIE LLM,word vector embeddings of hydropower station monitoring events are constructed and input into the hierarchical attention network to learn the features of each event.The classification results are then produced.The final fault diagnosis results are then obtained by verifying the results produced using regular expressions.[Results]The test results show that,compared with traditional monitoring alarm event diagnosis methods for hydropower stations,this model can improve the diagnostic accuracy by more than 2%,reduce the model training time by 30%,and achieve diagnosis within 0.1 seconds.[Conclusions]The proposed deep learning framework based on an LLM for enhancing alarm event samples and fault diagnosis of hydropower stations can achieve fast and accurate results during system failures,which is conducive to ensuring long-term stable and safe operation of the power system.关键词
SimBERT/正则表达式/文心一言/层级注意力网络/水电站智能告警Key words
SimBERT/regular expression/ERNIE/hierarchical attention network/hydropower station intelligent alarm分类
信息技术与安全科学引用本文复制引用
孙国强,史学恒,罗哲清,程礼临,臧海祥,卫志农..基于专家知识与大语言模型混合增强的水电站告警智能诊断[J].电力建设,2025,46(10):44-57,14.基金项目
国家自然科学基金项目(U24B2088) This work is supported by the National Natural Science Foundation of China(No.U24B2088). （U24B2088）
