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As traditional energy sources are increasingly depleted and clean energy sources such as wind power continue to develop, the number of wind turbines worldwide is growing. The blades of wind turbines are critical components, directly affecting the efficiency of wind energy conversion into electricity. Blade contamination not only impacts aerodynamic performance but also accelerates the aging of turbines, shortening their lifespan. This paper proposes a research method based on the Analytic Hierarchy Process (AHP) and Geographic Information System (GIS) technology to analyze the spatial distribution of blade contamination in onshore wind turbines worldwide. By analyzing key environmental factors such as PM2.5, PM10, precipitation, and wind speed from the MERRA-2 dataset, a comprehensive pollution assessment model is constructed. The study reveals that PM2.5 and PM10 are the primary pollutants affecting blade contamination, while precipitation and wind speed are key factors influencing the deposition and dispersion of pollutants. Using the AHP method, the weights of these factors are assigned, and a Blade Contamination Index is generated in conjunction with GIS technology. The results show that Europe and the Americas have relatively low contamination indices (0.1–0.2), while parts of Asia and Africa (particularly northern Africa, Central Asia, and Southwestern Asia) show higher contamination indices (0.4–0.9). These high-pollution areas are typically located in regions with harsh environmental conditions, severe pollution, and low rainfall, such as those frequently affected by sandstorms. The findings of this study provide a scientific basis for the early-stage planning of wind farms, helping to predict the impact of blade contamination on power generation efficiency and offering guidance for developing tailored wind turbine maintenance and cleaning strategies. The research also emphasizes the importance of differentiating wind turbine configurations based on regional pollution risks to enhance overall power generation efficiency.

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Study on the Spatial Distribution of Blade Contamination of Onshore Wind Turbines Worldwide Based on Multi-criteria Decision Analysis

  • 会议论文

  • 发表日期：

    2025年

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  • Qianqian Wei,
  • Yafei Li,
  • Ruomei Wang,
  • Meng Jin,
  • Chuhan Wang

摘要

As traditional energy sources are increasingly depleted and clean energy sources such as wind power continue to develop, the number of wind turbines worldwide is growing. The blades of wind turbines are critical components, directly affecting the efficiency of wind energy conversion into electricity. Blade contamination not only impacts aerodynamic performance but also accelerates the aging of turbines, shortening their lifespan. This paper proposes a research method based on the Analytic Hierarchy Process (AHP) and Geographic Information System (GIS) technology to analyze the spatial distribution of blade contamination in onshore wind turbines worldwide. By analyzing key environmental factors such as PM2.5, PM10, precipitation, and wind speed from the MERRA-2 dataset, a comprehensive pollution assessment model is constructed. The study reveals that PM2.5 and PM10 are the primary pollutants affecting blade contamination, while precipitation and wind speed are key factors influencing the deposition and dispersion of pollutants. Using the AHP method, the weights of these factors are assigned, and a Blade Contamination Index is generated in conjunction with GIS technology. The results show that Europe and the Americas have relatively low contamination indices (0.1–0.2), while parts of Asia and Africa (particularly northern Africa, Central Asia, and Southwestern Asia) show higher contamination indices (0.4–0.9). These high-pollution areas are typically located in regions with harsh environmental conditions, severe pollution, and low rainfall, such as those frequently affected by sandstorms. The findings of this study provide a scientific basis for the early-stage planning of wind farms, helping to predict the impact of blade contamination on power generation efficiency and offering guidance for developing tailored wind turbine maintenance and cleaning strategies. The research also emphasizes the importance of differentiating wind turbine configurations based on regional pollution risks to enhance overall power generation efficiency.