Wind turbines new criteria optimal site matching under new capacity factor probabilistic approaches

This paper introduces a novel methodology for determining wind turbines' optimal site matching. Our framework's key component is establishing four new probabilistic models to model wind turbines' capacity factors. First, we used the sum square of errors (SSE) and the determination coefficient (R²) to assess the accuracy of the proposed models. The developed models were then generalized to estimate wind farm capacity factors. After that, the proposed methodology's validity is tested by applying it to a known wind data site. We modeled the available wind data using Weibull distribution. Five different methods were used to determine Weibull parameters. The equivalent energy method combined with the invasive weed optimization algorithm yields the optimal values for these parameters. Finally, under the support of the presented models, the novel methodology is applied to a pool of 24 commercial turbines to choose the optimal ones. A comparison is made between the obtained capacity factor curves using the suggested models and other previous models for the selected wind turbines. When the presented models are utilized, the capacity factor of commercial wind turbines with non-ideal electrical power curves is more precisely predicted.