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LIN Jinhong, CHEN Zhenliang, WANG Songlin, et al. Prediction of bird collision risks in offshore wind farms based on the interaction between bird behavior and wind turbine parameters [J]. Southern energy construction, xxxx, x(): 1-7. DOI: 10.16516/j.ceec.2025-069
Citation: LIN Jinhong, CHEN Zhenliang, WANG Songlin, et al. Prediction of bird collision risks in offshore wind farms based on the interaction between bird behavior and wind turbine parameters [J]. Southern energy construction, xxxx, x(): 1-7. DOI: 10.16516/j.ceec.2025-069

Prediction of Bird Collision Risks in Offshore Wind Farms Based on the Interaction Between Bird Behavior and Wind Turbine Parameters

More Information
  • Received Date: March 11, 2025
  • Revised Date: April 13, 2025
  • Available Online: June 23, 2025
  • Objective 

    To address the conflict between offshore wind farm development and bird conservation, this study establishes a risk prediction model based on bird behavior-wind turbine parameter interactions, revealing spatiotemporal patterns of bird activity and collision mechanisms, thereby providing scientific decision-making support for eco-friendly wind power development.

    Method 

    Focusing on a coastal wind farm, we conducted 12-month bird activity monitoring using direct counting, high-definition photogrammetry, and GIS technology, and documented population dynamics and habitat preferences of 36 waterbird species. A Poisson distribution-based bird collision probability model was developed to quantify the interaction between season, flight altitude, and turbine operation parameters, and a risk stratification evaluation method was proposed.

    Result 

    The results show that, 1) Bird activity has significant seasonal variations, and summer is the high-risk period (540 egrets and 18 plovers in total) showing >50% collision probability concentrated at 20-50 m area in the middle and bottom part of the blade; 2) The flight altitude is negatively correlated with body weight (R2=0.87), except for high-altitude flying behavior in migration period, which causes risk distribution shifts; 3) The Poisson model can effectively quantify risk intensity coefficients (Σλ=749), and accurately identify high-risk periods (August) and key species (egrets contributing 97%).

    Conclusion 

    This study provides quantitative tools for environmental impact assessment of offshore wind power projects, facilitating synergistic development of energy exploitation and biodiversity conservation.

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