Electronics 2025, 14(22), 4502; https://doi.org/10.3390/electronics14224502 (registering DOI) - 18 Nov 2025
Abstract
By leveraging retrieval-augmented generation technology in human–computer interaction applications, a large language model was established based on local hardware to construct a root cause query model for equipment failures on virtual power plant intelligent operation and maintenance platforms. This enhances the efficiency of
[...] Read more.
By leveraging retrieval-augmented generation technology in human–computer interaction applications, a large language model was established based on local hardware to construct a root cause query model for equipment failures on virtual power plant intelligent operation and maintenance platforms. This enhances the efficiency of maintenance personnel in retrieving troubleshooting solutions from vast technical documentation. First, the virtual power plant architecture was established, clarifying the functions of each layer and defining the flow of information and commands between them. Subsequently, the time-based workflow and corresponding functional modules and sub-functions of its core smart operation and maintenance platform were analyzed. Then a root cause query model for equipment failures was developed on the local hardware platform. A knowledge base for equipment failure root causes was constructed. During deployment, two large models were selected for performance comparison. After comparative experiments, performance of RAG varied across different models, requiring careful selection based on hardware and environment to determine whether RAG technology should be employed.
Full article
(This article belongs to the Special Issue Intelligent Mobile Robotic Systems: Decision, Planning and Control, 2nd Edition)
►
Show Figures
Figure 1
