Improvement of power quality in WT-DFIG systems using novel direct power control based on fuzzy logic control under randomness conditions

ABSTRACTIn this paper, a novel direct power control technique founded on fuzzy logic controller (FLC-DPC) is selected to master and control the DFIG for wind energy conversion system (WECS). The fuzzy logic controller replaces both hysteresis regulators and the switching table in the proposed strategy. Seeking to enhance the control and overcome the defects associated with the conventional DPC (C-DPC) technique, this control depends on the errors of both active and reactive powers. The suitable rotor voltage vector for the inverter is obtained by FLC-DPC. The proposed control strategy is applied to the WT-DFIG system, in order to study its effectiveness. To reflect a real WECS operation, this study considers the wind’s random behaviour in successive and continuous ways throughout all WT-DFIG operating modes. Also, it takes into consideration all compensated local reactive power modes. The studied system and the proposed control were tested under MATLAB/Simulink environment. The obtained results showed the high effectiveness of the proposed control in terms of response time, robustness and ease. Consequently, C-DPC’s drawbacks are eliminated, and the ripples in compensated local reactive and produced active powers are reduced. Additionally, the total harmonic distortions (THDs) of injected currents are reduced, which improves their quality.KEYWORDS: Renewable energywind power conversion systemDFIGDPCfuzzy logic control Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsKarim Fathi SayehKarim Fathi Sayeh is currently a Ph.D. candidate in Renewable Energy Systems Control at the University of Bejaia, Algeria. He has a Master's in Electromechanical Engineering from the University of Djelfa, Algeria in 2021. His areas of research interest encompass Artificial Intelligence, Hybrid Renewable Energy Systems, Non-linear and Intelligent Control, as well as Energy Management.Salah TamalouztSalah Tamalouzt was born in Bejaia (Algeria). He received an engineering diploma in Electrical Engineering, specializing in Electrical Networks, and a Magister in Electrical Engineering, specializing in Power Electronics, from the University of Bejaia and the University of Batna, respectively. In 2017, he obtained his PhD diploma from the University of Bejaia. Since 2019, he has been a Senior Lecturer Class A and a Senior Researcher in the Electrical Engineering Department at the University. His research interests include Power Electronics, Modeling, Control and Management of Renewable Energy and Hybrid Energy Systems (such as Photovoltaic Systems, Wind Systems, Fuel Cells, Hydrogen), Hybrid Storage, Energy Management for Multi-Source Renewable Energy Systems, Supervision and Optimization of Micro-Grids, as well as Control and Optimization by Artificial Intelligence of Renewable Energy Systems, with a focus on Modeling and Control of Electric Machines and Drives.Younes SahriYounes Sahri was born in Mostaganem, Algeria. He received his Master's degree in Industrial Electrical Engineering from Université de Boumerdes, Algeria in 2017. He obtained a PhD (Doctorate) degree in Control and Energy Conversion from Université de Bejaia in 2022. He was an Assistant Professor & Researcher for 5 years at Université de Bejaia in the field of Electrical & Poder Control. His research interests include Renewable Energies; Electrical Machines; Nonlinear, Intelligent Power Control and Energy Management; Artificial Intelligence: Fuzzy/Neural Networks, Neuro-Fuzzy, Deep Learning & Reinforcement Learning; and Hybrid Energy Systems.