A New Stochastic Controller for Efficient Power Extraction from Small-Scale Wind Energy Conversion Systems under Random Load Consumption

Abdelhakim Tighirt, Mohamed Aatabe, Fatima El Guezar, Hassane Bouzahir, Alessandro N. Vargas and Gabriele Neretti ()
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Abdelhakim Tighirt: LISTI, National School of Applied Sciences, Ibn Zohr University, Agadir P.O. Box 1136, Morocco
Mohamed Aatabe: LISTI, National School of Applied Sciences, Ibn Zohr University, Agadir P.O. Box 1136, Morocco
Fatima El Guezar: LISTI, National School of Applied Sciences, Ibn Zohr University, Agadir P.O. Box 1136, Morocco
Hassane Bouzahir: LISTI, National School of Applied Sciences, Ibn Zohr University, Agadir P.O. Box 1136, Morocco
Alessandro N. Vargas: Labcontrol, Universidade Tecnológica Federal do Paraná, (UTFPR), Av. Alberto Carazzai 1640, Cornelio Procópio 86300-000, PR, Brazil
Gabriele Neretti: Department of Electrical, Electronic and Information Engineering, University of Bologna, 40136 Bologna, Italy

Energies, 2024, vol. 17, issue 19, 1-27

Abstract: This paper presents an innovative scheme to enhance the efficiency of power extraction from wind energy conversion systems (WECSs) under random loads. The study investigates how stochastic load consumption, modeled and predicted using a Markov chain process, impacts WECS efficiency. The suggested approach regulates the rectifier voltage rather than the rotor speed, making it a sensorless and reliable method for small-scale WECSs. Nonlinear WECS dynamics are represented using Takagi–Sugeno (TS) fuzzy modeling. Furthermore, the closed-loop system’s stochastic stability and recursive feasibility are guaranteed regardless of random load changes. The performance of the suggested controller is compared with the traditional perturb-and-observe (P&O) algorithm under varying wind speeds and random load variations. Simulation results show that the proposed approach outperforms the traditional P&O algorithm, demonstrating higher tracking efficiency, rapid convergence to the maximum power point (MPP), reduced steady-state oscillations, and lower error indices. Enhancing WECS efficiency under unpredictable load conditions is the primary contribution, with simulation results indicating that the tracking efficiency increases to 99.93 % .

Keywords: small-scale wind energy conversion system; maximum power point tracking; random loads; Markov chain model; stochastic control; sensorless technique (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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