This article presents a comparative study of adaptive filter–based power curve models to estimate wind turbine power output. In the real world, wind turbines are never subjected to ideal conditions; thus, adaptive filter–based power curves serve best when estimating the power in a time-varying environment. Adaptive filter–based power curve is implemented using various algorithms like least mean square, kernel least mean square, recursive least square, and kernel recursive least square algorithms. All models have been developed on National Renewable Energy Laboratory datasets. The performance of various models has been compared on the basis of parameters like mean absolute error, root mean square error, and R-squared score. In addition to this, the learning curves of each method have been obtained to show the performance variation over time.
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