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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。
Combining Predictive Control with Model Reduction Technology This paper proposes a nonlinear excitation predictive control method based on balanced reduced order model for multi-machine power system to solve the problem that the optimal excitation control and traditional proportional integral derivative excitation control can not consider the system complex state and control Input constraints, and reduce the complexity of numerical calculation of high-order dynamic model of nonlinear excitation predictive control. First of all, by using the experience of Gramians equilibrium reduction principle, the nonlinear dynamic model of power system is reduced in order to reduce the dimensionality of the dynamic model. Then, the excitation prediction control model based on the reduced order model is established. Taking the system input-output least square residual vector as the optimization target, the reduced-order dynamic model is taken as the equal constraint condition and the variable range of the output variable and control variable is used as the unequal constraint condition. Use interior point method to solve optimization problem. Finally, a four-machine power system is used to verify the effectiveness of the predictive control method. The simulation results show that the non-linear excitation predictive controller based on balanced reduced order model can greatly shorten the optimization calculation time and maintain the terminal voltage Near the value, to improve the stability of the system.