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Neural Network Predictive Control for Improved Reliability of Grid-Tied DFIG-Based Wind Energy System under the Three-Phase Fault Condition

Author

Listed:
  • Ramesh Kumar Behara

    (Electrical, Electronic, and Computer Engineering, University of KwaZulu-Natal, Durban 4041, South Africa)

  • Akshay Kumar Saha

    (Electrical, Electronic, and Computer Engineering, University of KwaZulu-Natal, Durban 4041, South Africa)

Abstract

This research explores a distinctive control methodology based on using an artificial neural predictive control network to augment the electrical power quality of the injection from a wind-driven turbine energy system, engaging a Doubly Fed Induction Generator (DFIG) into the grid. Because of this, the article focuses primarily on the grid-integrated wind turbine generation’s dependability and capacity to withstand disruptions brought on by three-phase circuit grid failures without disconnecting from the grid. The loading of the grid-integrated power inverter causes torque and power ripples in the DFIG, which feeds poor power quality into the power system. Additionally, the DC bus connection of the DFIG’s back-to-back converters transmits these ripples, which causes heat loss and distortion of the DFIG’s phase current. The authors developed a torque and power content ripple suppression mechanism based on an NNPC to improve the performance of a wind-driven turbine system under uncertainty. Through the DC bus linkage, it prevented ripples from being transmitted. The collected results are evaluated and compared to the existing control system to show the advancement made by the suggested control approach. The efficacy of the recommended control methodology for the under-investigation DFIG system is demonstrated through modelling and simulation using the MATLAB Simulink tool. The most effective control technique employed in this study’s simulations to check the accuracy of the suggested control methodology was the NNPC.

Suggested Citation

  • Ramesh Kumar Behara & Akshay Kumar Saha, 2023. "Neural Network Predictive Control for Improved Reliability of Grid-Tied DFIG-Based Wind Energy System under the Three-Phase Fault Condition," Energies, MDPI, vol. 16(13), pages 1-47, June.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:13:p:4881-:d:1177069
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    References listed on IDEAS

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    1. Yan Yan & Meng Wang & Zhan-Feng Song & Chang-Liang Xia, 2012. "Proportional-Resonant Control of Doubly-Fed Induction Generator Wind Turbines for Low-Voltage Ride-Through Enhancement," Energies, MDPI, vol. 5(11), pages 1-21, November.
    2. Ramesh Kumar Behara & Akshay Kumar Saha, 2022. "Artificial Intelligence Control System Applied in Smart Grid Integrated Doubly Fed Induction Generator-Based Wind Turbine: A Review," Energies, MDPI, vol. 15(17), pages 1-56, September.
    3. Igor Rodrigues de Oliveira & Fernando Lessa Tofoli & Victor Flores Mendes, 2022. "Thermal Analysis of Power Converters for DFIG-Based Wind Energy Conversion Systems during Voltage Sags," Energies, MDPI, vol. 15(9), pages 1-21, April.
    4. Santiago Arnaltes & Jose Luis Rodriguez-Amenedo & Miguel E. Montilla-DJesus, 2017. "Control of Variable Speed Wind Turbines with Doubly Fed Asynchronous Generators for Stand-Alone Applications," Energies, MDPI, vol. 11(1), pages 1-16, December.
    5. Nathan Oaks Farrar & Mohd Hasan Ali & Dipankar Dasgupta, 2023. "Artificial Intelligence and Machine Learning in Grid Connected Wind Turbine Control Systems: A Comprehensive Review," Energies, MDPI, vol. 16(3), pages 1-25, February.
    6. Ramesh Kumar Behara & Akshay Kumar Saha, 2022. "Artificial Intelligence Methodologies in Smart Grid-Integrated Doubly Fed Induction Generator Design Optimization and Reliability Assessment: A Review," Energies, MDPI, vol. 15(19), pages 1-39, September.
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