Wind Turbine Fault Maintenance Decision System Based on Knowledge Graphs and Large Language Models

Abstract

With the continuous expansion of China's power grid and the deepening of the new energy strategy, wind power is playing an increasingly critical role in the global energy supply. However, the traditional wind power operation and maintenance model can no longer meet the dual challenges of low fault diagnosis efficiency and high maintenance costs, making an intelligent transformation imperative. To address this, this paper targets the issues of low intelligence in wind power fault operation and maintenance and the heavy reliance on expert experience for decision-making. It proposes an intelligent decision-making system that integrates knowledge graphs and Large Language Models (LLMs) and elaborates on the construction method for domain-specific knowledge graphs. By designing the system architecture and integration technology, the advantages of this method in improving the efficiency and accuracy of operation and maintenance decision-making are validated. The contribution of this paper lies in proposing an operation and maintenance decision-making framework that synergizes knowledge graphs and LLMs, clarifying the key technological pathways, and providing theoretical reference and practical direction for the intelligentization of wind power operation and maintenance.