Proposal of a Hybrid Neuro-Fuzzy-Based Controller to Optimize the Energy Efficiency of a Wind Turbine

Nathalia-Michelle Peralta-Vasconez (), Leonardo Peña-Pupo, Pablo-Andrés Buestán-Andrade, José R. Nuñez-Alvarez and Herminio Martínez-García ()
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Nathalia-Michelle Peralta-Vasconez: Unidad Académica de Informática Ciencias de la Computación e Innovación Tecnológica, Universidad Católica de Cuenca, Cuenca 010107, Ecuador
Leonardo Peña-Pupo: Excellence Group in Thermal Power and Distributed Generation-NEST, Postgraduate Program in Energy Engineering, Institute of Mechanical Engineering, Federal University of Itajubá, Itajubá 37500-903, Brazil
Pablo-Andrés Buestán-Andrade: Unidad Académica de Informática Ciencias de la Computación e Innovación Tecnológica, Universidad Católica de Cuenca, Cuenca 010107, Ecuador
José R. Nuñez-Alvarez: Energy Department, Universidad de la Costa, Barranquilla 080002, Colombia
Herminio Martínez-García: Department of Electronic Engineering, Eastern Barcelona School of Engineering (EEBE), Universitat Politècnica de Catalunya—BarcelonaTech (UPC), E-08019 Barcelona, Spain

Sustainability, 2025, vol. 17, issue 8, 1-30

Abstract: Optimizing wind turbine control is a major challenge due to wind variability and nonlinearity. This research seeks to improve the performance of wind turbines by designing and developing hybrid intelligent controllers that combine advanced artificial intelligence techniques. A control system combining deep neural networks and fuzzy logic was implemented to optimize the efficiency and operational stability of a 3.5 MW wind turbine. This study analyzed several deep learning models (LSTM, GRU, CNN, ANN, and transformers) to predict the generated power, using data from the SCADA system. The structure of the hybrid controller includes a fuzzy inference system with 28 rules based on linguistic variables that consider power, wind speed, and wind direction. Experiments showed that the hybrid-GRU controller achieved the best balance between predictive performance and computational efficiency, with an R 2 of 0.96 and 12,119.54 predictions per second. The GRU excels in overall optimization. This study confirms intelligent hybrid controllers’ effectiveness in improving wind turbines’ performance under various operating conditions, contributing significantly to the field of wind energy.

Keywords: wind turbines; hybrid controllers; artificial intelligence; fuzzy logic; deep learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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