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Population extremal optimization-based extended distributed model predictive load frequency control of multi-area interconnected power systems

机译：基于人口极值优化的多区域互联电力系统扩展分布式模型预测负荷频率控制

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摘要

How to design a set of optimal distributed load frequency controllers for a multi-area interconnected power system is an important but still challenging issue in the field of modern electric power systems. This paper presents an adaptive population extremal optimization-based extended distributed model predictive load frequency control method called PEO-EDMPC for a multi-area interconnected power system. The key idea behind the proposed method is formulating the dynamic load frequency control issue of each area power system as an extended distributed discrete-time state-space model based on an extended state vector, obtaining a distributed dynamic extended predictive model, and rolling optimization of real-time control output signal by adopting an adaptive population extremal optimization algorithm, where the fitness is evaluated by the weighted sum of square predicted errors and square future control values. The superiority of the proposed PEO-EDMPC method to a traditional distributed model predictive control method, a population extremal optimization-based distributed proportional-integral control algorithm and a traditional distributed integral control method is demonstrated by the simulation studies on two-area and three-area interconnected power systems in cases of normal, perturbed system parameters and dynamical load disturbances. (c) 2018 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
机译：在现代电力系统领域中，如何为多区域互连电力系统设计一组最佳的分布式负载频率控制器是一个重要但仍具有挑战性的问题。本文提出了一种适用于多区域互联电力系统的基于自适应人口极值优化的扩展分布式模型预测负荷频率控制方法，称为PEO-EDMPC。该方法背后的关键思想是将每个区域电力系统的动态负载频率控制问题表达为基于扩展状态向量的扩展分布式离散时间状态空间模型，获得分布式动态扩展预测模型，以及滚动优化。通过采用自适应总体极值优化算法实时控制输出信号，其中适应性通过平方预测误差和平方未来控制值的加权总和进行评估。通过对两个区域和三个区域的仿真研究，证明了所提出的PEO-EDMPC方法优于传统的分布式模型预测控制方法，基于总体极值优化的分布式比例积分控制算法和传统的分布式积分控制方法。正常，扰动的系统参数和动态负载干扰的情况下，区域互连的电力系统。 （c）2018富兰克林研究所。由Elsevier Ltd.出版。保留所有权利。

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  • 来源
    《Journal of the Franklin Institute》 |2018年第17期|8266-8295|共30页
  • 作者

    Chen Min-Rong; Zeng Guo-Qiang; Xie Xiao-Qing;

  • 作者单位

    South China Normal Univ, Sch Comp, Guangzhou 510631, Guangdong, Peoples R China;

    Wenzhou Univ, Natl Local Joint Engn Lab Digitalize Elect Design, Wenzhou 325035, Peoples R China;

    Wenzhou Univ, Natl Local Joint Engn Lab Digitalize Elect Design, Wenzhou 325035, Peoples R China;

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