Specialized LLM optimizes wind farm maintenance planning, study finds

A research team has introduced a domain-tailored large language model named LLM4M to streamline wind farm maintenance decision-making, with findings published in Engineering. The work addresses longstanding limitations of conventional maintenance planning tools, which the paper notes consume substantial computation time and lack flexible adaptation to shifting operational scenarios with diverse turbine failure modes and variable maintenance expenditures. Wind farm operation and maintenance costs account for 30% of onshore wind turbines’ total life cycle cost, creating consistent demand for resource-efficient, low-cost scheduling frameworks amid expanding global wind power capacity.

The research frames wind turbine maintenance as a multi-team collaborative route optimization problem formulated as a mixed-integer linear programming (MILP) model, incorporating spatial travel costs, heterogeneous repair team capabilities, and distinct failure mode repair expenses. The team built LLM4M via labeled-data supervised fine-tuning based on the open-source Qwen2.5-7B-Instruct base model, adopting low-rank adaptation (LoRA) to adjust model weights without erasing pre-trained general knowledge. The workflow relies on custom structured prompt templates divided into scenario descriptions, parameter slots, output examples and constraint rules, which convert wind farm spatial layouts, fault information and crew specifications into standardized text inputs for model reasoning. The full five-step framework covers demand clarification, operational data extraction, structured prompt interaction, LLM deductive reasoning and executable maintenance plan delivery.

Benchmark tests compare LLM4M against the unmodified base model, mainstream general large-parameter LLMs including GPT-4o, Claude 3.5 Sonnet, DeepSeek-V3 and Qwen2.5-72B, alongside traditional heuristic optimization algorithms such as genetic algorithms, particle swarm optimization, ant colony optimization and simulated annealing. In fixed scenario testing with three maintenance teams and five faulty turbines, the fine-tuned LLM4M generates fully implementable maintenance plans in all test samples, while the unfine-tuned base model only produces valid schedules for 83.40% of test cases. The model achieves a mean absolute percentage error (MAPE) of 0.0200 relative to mathematically derived optimal cost solutions, outperforming all evaluated general LLMs whose MAPE values range between 0.1378 and 0.1580. When valid plans are generated, LLM4M’s maintenance cost deviation from optimal results sits at approximately 2%.

Additional tests across nonfixed scenarios with variable team counts and fault node volumes verify the model’s generalization capacity. Researchers identify clear phase transition behavior within model performance, showing qualitative shifts in solving ability once training datasets include at least three maintenance teams, an observation that offers actionable guidance for curating training corpora for maintenance-focused domain LLMs. Trials simulating dynamic real-time maintenance, where crews start from random turbine locations instead of a fixed depot, record implementable plan generation rates of 97.90% to 99.40% for adjusted test conditions. In deployment terms, LLM4M supports local offline operation with consumer-grade graphics hardware, removing recurring API access fees and network dependence that cloud-based general LLMs require. The paper outlines future research directions including dataset expansion covering weather constraints, integration of predictive and preventive maintenance logic, interpretability analysis for model decision pathways, and embedding physical engineering rules into fine-tuning pipelines.

The paper “Domain-Specific Large Language Model for Maintenance Decision-Making on Wind Farms by Labeled-Data-Supervised Fine-Tuning,” is authored by Dongming Fan, Meng Liu, Yi Shao, Linchao Yang, Yiliu Liu, Yue Zhang, Yi Ren, Zili Wang. Full text of the open access paper: https://doi.org/10.1016/j.eng.2025.12.019. For more information about Engineering, visit the website at https://www.sciencedirect.com/journal/engineering.