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<p>With the increasing penetration of renewable energy resources in power systems, conventional timescale separated load frequency control (LFC) and economic dispatch may degrade frequency performance and reduce economic efficiency. This paper proposes a novel data-driven adaptive distributed optimal disturbance rejection control (DODRC) method for real-time economic LFC problem in nonlinear power systems. Firstly, a basic DODRC method is proposed by integrating the active disturbance rejection control method and the partial primal–dual algorithm. Then, to deal with the tie-line power flow constraints, the logarithmic barrier function is employed to reconstruct the Lagrange function to obtain the constrained DODRC method. By analyzing the sensitivity of the uncertain parameters of power systems, a data-driven adaptive DODRC method is finally proposed with a neural network. The effectiveness of the proposed method is demonstrated by experimental results using real-time equipment.</p>

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Data-driven adaptive distributed optimal disturbance rejection control of frequency regulation in nonlinear power systems

  • 期刊论文

  • 发表日期：

    2025年8月12日

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  • Changhui Yu,
  • Xiao Qi,
  • Weixiong Wu,
  • Hui Deng,
  • Ming Du,
  • Wenguang Zhang,
  • Tianyu Wang

摘要

With the increasing penetration of renewable energy resources in power systems, conventional timescale separated load frequency control (LFC) and economic dispatch may degrade frequency performance and reduce economic efficiency. This paper proposes a novel data-driven adaptive distributed optimal disturbance rejection control (DODRC) method for real-time economic LFC problem in nonlinear power systems. Firstly, a basic DODRC method is proposed by integrating the active disturbance rejection control method and the partial primal–dual algorithm. Then, to deal with the tie-line power flow constraints, the logarithmic barrier function is employed to reconstruct the Lagrange function to obtain the constrained DODRC method. By analyzing the sensitivity of the uncertain parameters of power systems, a data-driven adaptive DODRC method is finally proposed with a neural network. The effectiveness of the proposed method is demonstrated by experimental results using real-time equipment.