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


Bilal B., Yetilmezsoy K., Ouassaid M.

ENERGIES, vol.17, no.3, pp.1-36, 2024 (SCI-Expanded)

  • Publication Type: Article / Article
  • Volume: 17 Issue: 3
  • Publication Date: 2024
  • Doi Number: 10.3390/en17030697
  • Journal Name: ENERGIES
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Agricultural & Environmental Science Database, CAB Abstracts, Communication Abstracts, Compendex, INSPEC, Metadex, Veterinary Science Database, Directory of Open Access Journals, Civil Engineering Abstracts
  • Page Numbers: pp.1-36
  • Yıldız Technical University Affiliated: Yes

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.