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Robust Load Frequency Control of Interconnected Power Systems with Back Propagation Neural Network-Proportional-Integral-Derivative-Controlled Wind Power Integration

Author

Listed:
  • Fang Ye

    (School of Electrical Engineering, Shenyang University of Technology, Shenyang 110020, China)

  • Zhijian Hu

    (LAAS-CNRS, Université de Toulouse, 31077 Toulouse, France
    School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore)

Abstract

As the global demand for energy sustainability increases, the scale of wind power integration steadily increases, so the system frequency suffers significant challenges due to the huge fluctuations of the wind power output. To address this issue, this paper proposes a Back Propagation Neural Network-Proportional-Integral-Derivative (BPNN-PID) controller to track the output power of the wind power generation system, which can well alleviate the volatility of the wind power output, resulting in the slighter imbalance with the rated wind power output. Furthermore, at the multi-area power system level, to mitigate the impact of the imbalanced wind power injected into the main grid, the H ∞ robust controller was designed to ensure the frequency deviation within the admissible range. Finally, a four-area interconnected power system was employed as the test system, and the results validated the feasibility and effectiveness of both the proposed BPNN-PID controller and the robust controller.

Suggested Citation

  • Fang Ye & Zhijian Hu, 2024. "Robust Load Frequency Control of Interconnected Power Systems with Back Propagation Neural Network-Proportional-Integral-Derivative-Controlled Wind Power Integration," Sustainability, MDPI, vol. 16(18), pages 1-17, September.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:18:p:8062-:d:1478611
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    References listed on IDEAS

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    1. Chapaloglou, Spyridon & Varagnolo, Damiano & Marra, Francesco & Tedeschi, Elisabetta, 2022. "Data-driven energy management of isolated power systems under rapidly varying operating conditions," Applied Energy, Elsevier, vol. 314(C).
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