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Nonlinear Control Strategies for Enhancing the Performance of DFIG-Based WECS under a Real Wind Profile

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  • Hamid Chojaa

    (Industrial Technologies and Services Laboratory, Higher School of Technology, Sidi Mohamed Ben Abdellah University, Fez 30000, Morocco)

  • Aziz Derouich

    (Industrial Technologies and Services Laboratory, Higher School of Technology, Sidi Mohamed Ben Abdellah University, Fez 30000, Morocco)

  • Mohammed Taoussi

    (Industrial Technologies and Services Laboratory, Higher School of Technology, Sidi Mohamed Ben Abdellah University, Fez 30000, Morocco)

  • Seif Eddine Chehaidia

    (Industrial Mechanics Laboratory, Badji Mokhtar Annaba University, P.O. Box 12, Annaba 23000, Algeria)

  • Othmane Zamzoum

    (Industrial Technologies and Services Laboratory, Higher School of Technology, Sidi Mohamed Ben Abdellah University, Fez 30000, Morocco)

  • Mohamed I. Mosaad

    (Yanbu Industrial College (YIC), Alnahdah, Yanbu Al Sinaiyah, Yanbu 46452, Saudi Arabia)

  • Ayman Alhejji

    (Yanbu Industrial College (YIC), Alnahdah, Yanbu Al Sinaiyah, Yanbu 46452, Saudi Arabia)

  • Mourad Yessef

    (Laboratory of Engineering, Modeling and Systems Analysis, SMBA University, Fez 30000, Morocco)

Abstract

Wind speed variations affect the performance of the wind energy conversion systems (WECSs) negatively. This paper addressed an advanced law of the backstepping controller (ABC) for enhancing the integration of doubly fed induction generator (DFIG)-based grid-connected WECS under wind range of wind speed. This enhancement was achieved through three control schemes, which were blade pitch control, rotor-side control, and grid-side control. The blade pitch control was presented to adjust the wind turbine speed when the wind speed exceeds its rated value. In addition, the rotor and grid-side converter controllers were presented for improving the direct current link voltage profile and achieving maximum power point tracking (MPPT) under speed variations, respectively. To evaluate the effectiveness of the proposed ABC control, a comparison between PI and sliding-mode control (SMC) was presented, considering the parameters of a 1.5 MW DFIG wind turbine in the Assilah zone in Morocco. Moreover, some changes in the DFIG parameters were introduced to investigate the robustness of the proposed controller under parameter uncertainties. Simulation results showed the capability of the proposed ABC controller to enhance the performance of the DFIG-WECS based on variable speed and variable pitch turbine, at both below and above-rated speed, leading to an error around 10 −3 (p.u), with an ATE = 0.4194 in the partial load region; in terms of blade pitch control, an error of 2.10 −4 (p.u) was obtained, and the DC-link voltage profile showed a measured performance of 5 V and remarkable THD value reduction compared to other techniques, with a measured THD value of 2.03%, 1.67%, and 1.46% respectively, in hyposynchronous, hypersynchronous, and pitch activation modes of operation. All simulations were performed using MATLAB/SIMULINK based on real wind profiles in order to make an exhaustive analysis with realistic operating conditions and parameters.

Suggested Citation

  • Hamid Chojaa & Aziz Derouich & Mohammed Taoussi & Seif Eddine Chehaidia & Othmane Zamzoum & Mohamed I. Mosaad & Ayman Alhejji & Mourad Yessef, 2022. "Nonlinear Control Strategies for Enhancing the Performance of DFIG-Based WECS under a Real Wind Profile," Energies, MDPI, vol. 15(18), pages 1-23, September.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:18:p:6650-:d:912530
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    References listed on IDEAS

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    1. Kelkoul, Bahia & Boumediene, Abdelmadjid, 2021. "Stability analysis and study between classical sliding mode control (SMC) and super twisting algorithm (STA) for doubly fed induction generator (DFIG) under wind turbine," Energy, Elsevier, vol. 214(C).
    2. Kumar, Dipesh & Chatterjee, Kalyan, 2016. "A review of conventional and advanced MPPT algorithms for wind energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 957-970.
    3. Yu-Hsiang Hung & Yi-Wei Chen & Cheng-Han Chuang & Yuan-Yih Hsu, 2021. "PSO Self-Tuning Power Controllers for Low Voltage Improvements of an Offshore Wind Farm in Taiwan," Energies, MDPI, vol. 14(20), pages 1-15, October.
    4. Belmokhtar, K. & Doumbia, M.L. & Agbossou, K., 2014. "Novel fuzzy logic based sensorless maximum power point tracking strategy for wind turbine systems driven DFIG (doubly-fed induction generator)," Energy, Elsevier, vol. 76(C), pages 679-693.
    5. Ahmed G. Abo-Khalil & Saeed Alyami & Khairy Sayed & Ayman Alhejji, 2019. "Dynamic Modeling of Wind Turbines Based on Estimated Wind Speed under Turbulent Conditions," Energies, MDPI, vol. 12(10), pages 1-25, May.
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