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Benchmarking of Various Flexible Soft-Computing Strategies for the Accurate Estimation of Wind Turbine Output Power

Author

Listed:
  • Boudy Bilal

    (Electrical Engineering Department, UR-EEDD, Ecole Supérieure Polytechnique, Nouakchott BP 4303, Mauritania
    URAER/FST, Université de Nouakchott, Nouakchott BP 5026, Mauritania)

  • Kaan Yetilmezsoy

    (Department of Environmental Engineering, Faculty of Civil Engineering, Yildiz Technical University, Davutpasa, Esenler, 34220 Istanbul, Turkey)

  • Mohammed Ouassaid

    (Electrical Engineering Department, Engineering for Smart and Sustainable Systems Research Centre, Mohammadia School of Engineers, Mohammed V University in Rabat, Rabat 10090, Morocco)

Abstract

This computational study explores the potential of several soft-computing techniques for wind turbine (WT) output power (kW) estimation based on seven input variables of wind speed (m/s), wind direction (°), air temperature (°C), pitch angle (°), generator temperature (°C), rotating speed of the generator (rpm), and voltage of the network (V). In the present analysis, a nonlinear regression-based model (NRM), three decision tree-based methods (random forest (RF), random tree (RT), and reduced error pruning tree (REPT) models), and multilayer perceptron-based soft-computing approach (artificial neural network (ANN) model) were simultaneously implemented for the first time in the prediction of WT output power (WTOP). To identify the top-performing soft computing technique, the applied models’ predictive success was compared using over 30 distinct statistical goodness-of-fit parameters. The performance assessment indices corroborated the superiority of the RF-based model over other data-intelligent models in predicting WTOP. It was seen from the results that the proposed RF-based model obtained the narrowest uncertainty bands and the lowest quantities of increased uncertainty values across all sets. Although the determination coefficient values of all competitive decision tree-based models were satisfactory, the lower percentile deviations and higher overall accuracy score of the RF-based model indicated its superior performance and higher accuracy over other competitive approaches. The generator’s rotational speed was shown to be the most useful parameter for RF-based model prediction of WTOP, according to a sensitivity study. This study highlighted the significance and capability of the implemented soft-computing strategy for better management and reliable operation of wind farms in wind energy forecasting.

Suggested Citation

  • Boudy Bilal & Kaan Yetilmezsoy & Mohammed Ouassaid, 2024. "Benchmarking of Various Flexible Soft-Computing Strategies for the Accurate Estimation of Wind Turbine Output Power," Energies, MDPI, vol. 17(3), pages 1-36, February.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:3:p:697-:d:1331018
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    References listed on IDEAS

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