摘要
介绍一种基于平衡降阶理论的电力系统多机励磁预测控制方法。为减少开环优化计算时间,利用平衡降阶理论对电力系统多机线性模型进行降阶,降低预测模型维数。以预测步长内系统输出和控制量的偏差最小为优化控制指标,以模型降阶微分方程、状态和输入为约束条件,利用改进内点法求解优化问题。从降低模型阶数和改进优化算法两方面降低计算复杂度,加快在线计算速度,适用于电力系统大规模励磁控制。对某50机系统进行仿真验证该方法的有效性,仿真结果表明:基于平衡降阶模型的励磁预测控制器可极大降低优化计算时间,提高系统的稳定性。
An excitation predictive control method for multi-machine power system based on balanced reduction model is presented. In order to shorten the open-loop optimization calculating time of model predictive control (MPC), the theory of balanced reduction is used to reduce the orders of multi-machine power system linear dynamic model. It uses the least-square residual of system output and control variables as the objection function, using reduced dynamic model and the change limits of system states and inputs as constraints. Then, the improved interior-point method is used to solve the optimal problem. The optimization calculation of MPC can be sped up from two aspects of the reduction of the dynamic model orders and the improvement of the optimization algorithm, and it is suitable for excitation control of large-scale power system. Finally, a fifty-machine power system is used to verify the effectiveness of the presented method. The simulation results show that excitation predictive control method based on balanced reduction model can greatly shorten the optimization calculating time and improve the stability of power system.
出处
《智能电网》
2015年第5期388-393,共6页
Smart Grid
基金
国家自然科学基金项目(51077053)~~
关键词
多机系统
模型预测控制
励磁控制
平衡降阶
multi-machine power system
model predictive control
excitation control
balanced reduction
