Tube robust model predictive control of load frequency for an interconnected power system with wind power based on PDNN

被引：0
|
作者：
Zhang H. [1 ]
Yang Y. [1 ]
Wang Y. [1 ]
Yuan L. [1 ]
Jiang D. [2 ]
机构：
[1] School of Electrical Engineering, Northeast Electric Power University, Jilin
[2] School of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin
来源：
Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control | 2020年 / 48卷 / 12期
基金：
中国国家自然科学基金;
关键词：
Load frequency control (LFC); Primal-dual neural network; Tube robust model predictive control (Tube-RMPC); Wind power uncertainty;
D O I：
10.19783/j.cnki.pspc.190892
中图分类号：
学科分类号：
摘要：
With the large-scale access of new energy to the power grid, in order to effectively solve the uncertainty of load frequency control caused by the energy's randomness and fluctuation, and to realize the optimal operation of the Load Frequency Control (LFC) system with multiple constraints, an LFC polytopicl model with wind turbines is established to reduce the influence of model parameter uncertainty on the control system. The Tube-Robust Model Predictive Control (Tube-RMPC) strategy based on the Primal-Dual Neural Network (PDNN) is designed. Combining a nominal model predictive controller with an auxiliary feedback controller, the nominal model predictive controller is solved by PDNN in real time to ensure an optimal state trajectory for the LFC system. The auxiliary feedback controller is designed to counteract external disturbances so as to control the state of the actual system to be maintained in the tube with the nominal trajectory as the center. Finally, the simulation results of a three-area system with wind power shows that the proposed Tube-RMPC control strategy can not only effectively improve control accuracy, but also enhance the robustness of the system and improve the efficiency of real-time optimization. © 2020, Power System Protection and Control Press. All right reserved.
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页码：137 / 146
页数：9
相关论文
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