161 / 2025-06-06 21:20:36

Monitoring Sensor Prediction of Power Transformer based on Domain Specific Large Language Model: An Experimental Case Study

Large Language Model (LLM),power transformer,Monitoring Sensor Prediction,oil temperature prediction

Special Sessions > SSL 08 New perspective of intelligent detection, diagnosis, prognosis, and maintenance: Generative AI and Industrial Large Model

Monitoring sensor prediction is a critical component of power transformer operation and maintenance. Traditional time-series-based methods for predicting variables such as oil temperature suffer from two major limitations: (a) they require domain-specific expertise, as engineers must understand the algorithm's underlying principles and parameter configurations; (b) they cannot leverage textual knowledge documents. To address the first issue, this study fine-tunes a large language model (LLM) for sensor prediction, leveraging their conversational interface, which allows engineers to obtain prediction results simply by posing queries. This fine-tuning is achieved through Low-Rank Adaptation (LoRA), a method that accelerates training and efficiently updates parameters. To address the second issue, we employ retrieval-augmented generation (RAG) techniques to enable the integration of local knowledge bases. This transforms the LLM into a domain-specific expert capable of reading and utilizing maintenance manuals, technical patents, and operations and maintenance (O&M) logs to provide informed expert operational suggestions. Experiments and visualizations validate the effectiveness of the proposed approach, highlighting the potential of LLMs in the field of power system maintenance.