Intelligent Operation and Maintenance and Prediction Model Optimization
  for Improving Wind Power Generation Efficiency
Authors: name: Xun Liu, name: Xiaobin Wu, name: Jiaqi He, name: Rajan Das Gupta

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.