【摘要】 随着新能源大规模接入电网,为应对新能源随机性和波动性给互联系统负荷频率控制(Load Frequency Control, LFC)带来的不确定问题,实现新能源电力系统多约束条件下的优化运行,建立了含风电机组的LFC多胞模型,以减少模型参数不确定对控制系统的影响。设计了基于原对偶神经网络(Primal-Dual Neural Network, PDNN)的Tube鲁棒模型预测控制(Tube-Robust Model Predictive Control, Tube-RMPC)策略。将标称模型预测控制器与辅助反馈控制器结合,通过PDNN实时求解标称模型预测控制器以保证为LFC系统产生最优状态轨迹。设计辅助反馈控制器抵消外部干扰,使实际系统的状态维持在以标称轨迹为中心的Tube内。最后,对含风电的三区域负荷频率控制系统进行仿真研究,结果表明所提出的Tube-RMPC控制策略,不仅能够有效提高控制精度,还能增强系统鲁棒性,提高实时优化效率。更多还原

【Abstract】 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.更多还原