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Abstract. This study investigates how blade aerodynamic modifications, including Leading Edge Roughness (LER), influence offshore wind turbine performance over their operational lifespan. Developing a novel methodology, this research analyses data from twelve multi-megawatt turbines over a twelve-year period, focusing on the intricate relationship between blade erosion, blade enhancements, operations and maintenance events, control PLC parameter updates, and their cumulative impact on turbine efficiency. The analysis hinges on the integration of SCADA data, Operations and Maintenance (O\) records, and air density corrections. A key contribution is the development of a Turbine Performance Integral (TPI) method, which leverages generator speed and power output data to track performance trajectories. Seasonal-Trend decomposition using Locally Estimated Scatterplot Smoothing (STL) further isolates long-term trends and seasonal variations in performance. Overcoming data availability and quality limitations, the study reveals significant findings concerning software updates impacts on turbine control strategies, the variable effects of blade repairs and enhancements and the complex interaction between O\ events and performance. This study's strength lies in its methodical approach and statistical rigour, offering a path forward in the quest for optimised wind turbine efficiency and advancing renewable energy.
Malik et al. (2024) studied this question.