Advanced power coefficient modeling for stall-regulated wind turbines

This paper introduces a novel parametric model for estimating the power coefficient of stall-regulated wind turbines as a function of tip speed ratio and pitch angle. The proposed model improves upon existing polynomial, sinusoidal, and exponential formulations by incorporating the variation in drag-to-lift ratio, which significantly enhances the accuracy of power predictions. Model calibration is performed using a deterministic nonlinear least squares method to ensure robust parameter estimation. The enhanced model achieves a notable increase in predictive accuracy, with the adjusted R 2 improving from 0.93 to 0.96 and the root mean square error decreasing from 0.41 kW to 0.33 kW.

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Publication Details

Journal
Wind Engineering
Published
2025-08-31
DOI
https://doi.org/10.1177/0309524x251375306
Citations
2
Primary Topic
Wind Energy Research and Development
Type
article
Field-Weighted Citation Impact
9.99
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article

Advanced power coefficient modeling for stall-regulated wind turbines

Abdelaziz Arbaoui, Amar Amour
2 citations
Wind Engineering
Wind Energy Research and Development
9.99
article

Advanced power coefficient modeling for stall-regulated wind turbines

Abdelaziz Arbaoui, Amar Amour
article en
2 citations

Abstract

This paper introduces a novel parametric model for estimating the power coefficient of stall-regulated wind turbines as a function of tip speed ratio and pitch angle. The proposed model improves upon existing polynomial, sinusoidal, and exponential formulations by incorporating the variation in drag-to-lift ratio, which significantly enhances the accuracy of power predictions. Model calibration is performed using a deterministic nonlinear least squares method to ensure robust parameter estimation. The enhanced model achieves a notable increase in predictive accuracy, with the adjusted R 2 improving from 0.93 to 0.96 and the root mean square error decreasing from 0.41 kW to 0.33 kW.

Wind Engineering
Université Moulay Ismail de Meknes (MA)
Openalex Percentile: Top 2%
Wind Energy Research and Development
9.99
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