中文摘要：

将预测控制与模型降阶技术相结合提出一种基于平衡降阶模型的多机电力系统非线性励磁预测控制方法,以解决最优励磁控制和传统比例积分微分励磁控制无法考虑系统复杂状态和控制输入约束的问题,并且降低非线性励磁预测控制高阶动态模型数值计算的复杂性。首先,利用经验Gramians平衡降阶原理,对电力系统非线性动态模型进行降阶,以降低动态模型的维数。然后,建立基于降阶模型的励磁预测控制模型。以系统输入输出最小二乘残差向量为优化目标,以降阶动态模型作为等约束条件,以输出量、控制量的变化范围作为不等约束条件。利用内点法求解优化问题。最后,利用一个四机电力系统验证该预测控制方法的有效性,仿真结果表明：基于平衡降阶模型的多机电力系统非线性励磁预测控制器能够大大缩短优化计算时间,维持机端电压在定值附近,提高系统的稳定性。

英文摘要：

This paper presented a multi-machine power system nonlinear excitation predictive control method which combined model predictive control and model reduction technology in order to tackle the problem that the optimal excitation control and the traditional proportional-integralderivative（PID） excitation control could not consider the constraint of states and input of the system,and to reduce the complexity of the numerical calculation of high order dynamic model in nonlinear excitation predictive control.First,The theory of empirical Gramians balanced reduction was used to reduce the orders of power system nonlinear dynamic model to save the computing time of open-loop optimization of model predictive control.Then,it used the least-square residual of system input and output as the objection function,using reduced dynamic model as equivalent constraint and the change limits of system output and control input as unequivalent constrain to establish the excitation predictive control model based on reduced model.Next,the interior-point method was used to solve the optimal problem meanwhile to realize multi-step prediction.Finally,we took advantage of a four-machine power system to verify the effectiveness of the predictive control method.The simulation results show that nonlinear excitation predictive control method based on balanced reduced model for the multi-machine power systems can greatly shorten the optimization time,meanwhile maintain the voltage of generator terminals within the set points and improve the stability of power system.