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Fuzzy-neural-network-based model-free load frequency control of multi-area power systems with hybrid energy storage under wind penetration

Author

Listed:
  • Zeng, Yiming
  • Xu, Dezhi
  • Ji, Xunsheng
  • Shen, Qikun
  • Bu, Xuhui
  • Yang, Weilin

Abstract

This paper proposes a method based on data-driven control theory to solve the frequency control problem. Wind power as a form of clean energy has also been introduced into the power system. Paired with a hybrid energy storage system (HESS), the studied power system is enriched with a solution to the load frequency control (LFC) problem. Depending entirely on the HESS input-output (I/O) data, a data-driven LFC algorithm has been developed. Initially, the discrete power system is programmed using a linearisation technique to produce a dynamic linear relationship based on I/O data. Next, training of fuzzy neural network (FNN) using I/O data is conducted to estimate the power system pseudo-partial derivative (PPD). Considering wind power stochasticity on the power system, a model-free adaptive control (MFAC) algorithm is combined with the sliding mode control (SMC) theory to enhance the robustness of frequency control. Using the three-area HESS-integrated power system under wind penetration as a simulation model, the results verify the effectiveness of the designed LFC algorithm in solving load disturbances, demonstrating its robustness under various operating conditions.

Suggested Citation

  • Zeng, Yiming & Xu, Dezhi & Ji, Xunsheng & Shen, Qikun & Bu, Xuhui & Yang, Weilin, 2026. "Fuzzy-neural-network-based model-free load frequency control of multi-area power systems with hybrid energy storage under wind penetration," Applied Energy, Elsevier, vol. 404(C).
    • Handle: RePEc:eee:appene:v:404:y:2026:i:c:s0306261925018549
    DOI: 10.1016/j.apenergy.2025.127124
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    References listed on IDEAS

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    1. Yang, Jin & Zhong, Qishui & Ghias, Amer M.Y.M. & Dong, Zhao Yang & Shi, Kaibo & Yu, Yongbin, 2023. "Distributed fault-tolerant PI load frequency control for power system under stochastic event-triggered scheme," Applied Energy, Elsevier, vol. 351(C).
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    5. Li, Jiawen & Yu, Tao & Zhang, Xiaoshun, 2022. "Coordinated load frequency control of multi-area integrated energy system using multi-agent deep reinforcement learning," Applied Energy, Elsevier, vol. 306(PA).
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