To address these issues, this study systematically explores an intelligent operation and maintenance method for wind turbines, utilizing digital twin technology and multi-source data fusion..
To address these issues, this study systematically explores an intelligent operation and maintenance method for wind turbines, utilizing digital twin technology and multi-source data fusion..
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. .
Wind turbine operation and maintenance (O&M) faces significant challenges due to the complexity of equipment, harsh operating environments, and the difficulty of real-time fault prediction. Traditional methods often fail to provide timely and accurate warnings, leading to increased downtime and. .
🚀 This paper presents a qualitative analysis of predictive maintenance models and intelligent O&M system optimization for wind power. Based on insights from experienced wind farm engineers, it explores real-world challenges—such as false positives, sensor faults, and integration issues with legacy. .
Objective At present, domestic and international research on high-altitude wind power generation technology has been carried out, mainly in the fields of operation control, mechanism analysis and system design, but no research results have been found in the field of high-altitude wind power station.
