基于神经网络的电力系统暂态稳定分布式自适应控制
作者:
作者单位:

1.华东交通大学电气学院;2.University of Essex,United Kingdom

作者简介:

通讯作者:

中图分类号:

TP273

基金项目:

国家自然科学基金(11662002),江西省科技厅项目(20182BCB22009,20165BCB19011,20171BAB202029)资助;


Neural Network-Based Distributed Adaptive Control for Power System Transient Stability
Author:
Affiliation:

East China Jiaotong University

Fund Project:

Supported by National Natural Science Foundation of China(11662002),Project of Science and Technology Department of Jiangxi Province(20182BCB22009,20165BCB19011,20171BAB202029)

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    针对电力系统中普遍存在的系统非线性和参数不确定性等问题,本文提出了一种基于径向基函数神经网络(RBFNN)的分布式自适应控制器,以提高多机电力系统的暂态稳定性。利用基于RBFNN的方法对系统中的未知非线性项和外部扰动进行补偿,设计了相应的自适参数估计方法,逼近未知非线性项的理想权值矩阵。该策略基于多智能体框架,分布式控制器通过通信网络接收PMU测量的实时数据,并控制储能装置动作,使受到扰动后各发电机能够迅速实现频率同步。利用李雅普诺夫稳定性理论,证明了所提出的分布式控制方法的收敛性。最后,通过仿真研究验证了所提出的分布式控制方法的有效性。

    Abstract:

    A distributed adaptive controller based on Radial Basis Function Neural Network (RBFNN) is proposed to enhancing the transient stability of power system for the system nonlinearity and parameter uncertainty prevalent in power systems. The unknown nonlinear term and the external disturbance term in the systems are compensated by using the radial basis function neural networks method, and the corresponding adaptive parameter estimation scheme is designed to approximate the ideal weight matrix of the unknown nonlinearity. The strategy is based on multi-agent framework. Distributed controller receives real-time data from PMU measurement through communication network and controls the action of energy storage device, so that each generator can realize frequency synchronization quickly after disturbance. The convergence of the proposed distributed control method is proved by Lyapunov stability theory. Finally, the effectiveness of the proposed distributed control method is verified by simulation studies.

    参考文献
    相似文献
    引证文献
引用本文
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2019-08-16
  • 最后修改日期:2020-12-21
  • 录用日期:2020-02-29
  • 在线发布日期:
  • 出版日期: