Intelligent Diagnosis of Hydroelectric Power Station Alarm Based on Hybrid-augmentation of Expert Knowledge and Large Language Model

SUN Guoqiang; SHI Xueheng; LUO Zheqing; CHENG Lilin; ZANG Haixiang; WEI Zhinong

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Intelligent Diagnosis of Hydroelectric Power Station Alarm Based on Hybrid-augmentation of Expert Knowledge and Large Language Model

SUN Guoqiang¹, SHI Xueheng¹, LUO Zheqing², CHENG Lilin¹, ZANG Haixiang¹, WEI Zhinong¹

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Abstract

[Objective] In order to address the problem 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 large language model for hydropower station alarm event sample enhancement and fault diagnosis is designed. At the same time, regular expressions based on expert knowledge are introduced to verify the alarm diagnosis results and improve the credibility of the alarm event diagnosis results. [Methods] Firstly, considering the issue of imbalanced monitoring event samples in hydropower station, the SimBERT model is used to enhance the number of data for types with fewer samples. The enhanced data is then mixed with the original data to form the input samples of the deep learning network; Secondly, based on power grid monitoring alarm rules, constructing regular expressions for typical faults to achieve accurate discrimination of typical faults; Finally, based on the ERNIE large language model, word vector embeddings of hydropower station monitoring events are constructed, and are input into the hierarchical attention network to learn features of each event. The classification results are then produced. The final fault diagnosis results are then obtained by verifying with results produced by regular expressions. [Results] Test results show that compared with traditional monitoring alarm event diagnosis methods for hydropower stations, this model can improve diagnostic accuracy by more than 2%, reduce model training time by 30%, and achieve diagnosis time within 0.1 seconds. [Conclusions] The proposed deep learning framework based on large language model for enhancing alarm event samples and fault diagnosis of hydropower stations can achieve fast and accurate diagnosis of fault event types during system failures, which is conducive to ensuring the long-term stable and safe operation of the power system.

Key words

SimBERT / regular expression / ERNIE / hierarchical attention network / hydropower station intelligent alarm

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SUN Guoqiang, SHI Xueheng, LUO Zheqing, CHENG Lilin, ZANG Haixiang, WEI Zhinong. Intelligent Diagnosis of Hydroelectric Power Station Alarm Based on Hybrid-augmentation of Expert Knowledge and Large Language Model[J]. Electric Power Construction. 0

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