Intelligent Operation and Maintenance and Prediction Model Optimization for Improving Wind Power Generation Efficiency
Xun Liu, Xiaobin Wu, Jiaqi He, Rajan Das Gupta
2025-06-25
Summary
This study talks about how intelligent operation and maintenance (O&M) systems and predictive maintenance models can help improve the efficiency of wind power generation by keeping wind turbines running smoothly.
What's the problem?
The problem is that while predictive maintenance can identify major faults and reduce downtime, it often struggles to detect smaller, gradual problems. Additionally, issues like false alarms from sensors, sensor failures, and difficulties in combining new technology with older turbines limit the effectiveness of these systems.
What's the solution?
The researchers conducted interviews with experienced wind farm engineers and maintenance managers and analyzed their insights to understand the challenges better. They found that digital tools like digital twins, SCADA systems, and condition monitoring have improved turbine maintenance but still need better AI and real-time data integration to be fully effective.
Why it matters?
This matters because improving maintenance and prediction models helps reduce unexpected turbine breakdowns, cut maintenance costs, and increase the overall power generated from wind farms, which supports the growth of renewable energy and a cleaner environment.
Abstract
This study explores the effectiveness of predictive maintenance models and the optimization of intelligent Operation and Maintenance (O&M) systems in improving wind power generation efficiency. Through qualitative research, structured interviews were conducted with five wind farm engineers and maintenance managers, each with extensive experience in turbine operations. Using thematic analysis, the study revealed that while predictive maintenance models effectively reduce downtime by identifying major faults, they often struggle with detecting smaller, gradual failures. Key challenges identified include false positives, sensor malfunctions, and difficulties in integrating new models with older turbine systems. Advanced technologies such as digital twins, SCADA systems, and condition monitoring have significantly enhanced turbine maintenance practices. However, these technologies still require improvements, particularly in AI refinement and real-time data integration. The findings emphasize the need for continuous development to fully optimize wind turbine performance and support the broader adoption of renewable energy.