Quantification of the performance degradation on the annual energy production (AEP) of a wind farm due to leading‐edge (LE) erosion of wind turbine blades is important to design cost‐effective maintenance plans and timely blade retrofit. In this work, the effects of LE erosion on horizontal axis wind turbines are quantified using infrared (IR) thermographic imaging of turbine blades, as well as meteorological and SCADA data. The average AEP loss of turbines with LE erosion is estimated from SCADA and meteorological data to be between 3% and 8% of the expected power capture. The impact of LE erosion on the average power capture of the turbines is found to be higher at lower hub‐height wind speeds (peak around 50% of the turbine rated wind speed) and at lower turbulence intensity of the incoming wind associated with stable atmospheric conditions. The effect of LE erosion is investigated with IR thermography to identify the laminar to turbulent transition (LTT) position over the airfoils of the turbine blades. Reduction in the laminar flow region of about 85% and 87% on average in the suction and pressure sides, respectively, is observed for the airfoils of the investigated turbines with LE erosion. Using the observed LTT locations over the airfoils and the geometry of the blade, an average AEP loss of about 3.7% is calculated with blade element momentum simulations, which is found to be comparable with the magnitude of AEP loss estimated through the SCADA data.
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Abstract The maintenance of wind farms is one of the major factors affecting their profitability. During preventive maintenance, the shutdown of wind turbines causes downtime energy losses. The selection of when and which turbines to maintain can significantly impact the overall downtime energy loss. This paper leverages a wind farm power generation model to calculate downtime energy losses during preventive maintenance for an offshore wind farm. Wake effects are considered to accurately evaluate power output under specific wind conditions. In addition to wind speed and direction, the influence of wake effects is an important factor in selecting time windows for maintenance. To minimize the overall downtime energy loss of an offshore wind farm caused by preventive maintenance, a mixed‐integer nonlinear optimization problem is formulated and solved by the genetic algorithm, which can select the optimal maintenance time windows of each turbine. Weather conditions are imposed as constraints to ensure the safety of maintenance personnel and transportation. Using the climatic data of Cape Cod, Massachusetts, the schedule of preventive maintenance is optimized for a simulated utility‐scale offshore wind farm. The optimized schedule not only reduces the annual downtime energy loss by selecting the maintenance dates when wind speed is low but also decreases the overall influence of wake effects within the farm. The portion of downtime energy loss reduced due to consideration of wake effects each year is up to approximately 0.2% of the annual wind farm energy generation across the case studies—with other stated opportunities for further profitability improvements.
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