中文摘要：

提出基于在线递推闭环子空间辨识的模型预测阻尼控制器设计方法。通过辨识获得包含主导低频振荡模式的系统降阶状态空间模型；通过模型预测和优化求解，得到以当前系统状态为初始状态的无限时域的闭环最优控制量。模型辨识和控制量的优化求解在有限时间间隔内反复进行。该方法克服了基于离线辨识设计的固定参数控制器的固有缺点，解决了由于运行方式复杂多变和参数的不确定性与时变性引起的控制性能降低问题。8机36节点系统仿真结果表明，控制器可有效地抑制系统的区间低频振荡，并具有与电力系统稳定器（power system stabilizer，PSS）和其他预测阻尼控制器相互协调和适应运行方式变化的能力。

英文摘要：

Based on online recursive closed-loop subspace identification, a model predictive damping controller design strategy was proposed. The reduced order state space model which contains dominant low frequency oscillation modes was firstly identified. According to model prediction and optimization with the current state of power system as the initial state, an infinite horizon closed-loop optimal control was obtained. Online model identification and control optimization were repeated in each time interval. The strategy overcomes the inherent shortcomings of controllers with fixed parameters based on offline identification thus solves the problem of the control performance degradation due to variation of the complex operation conditions and time-varying and uncertain characteristic of system parameters. Simulation results of the China EPRI 8-machine 36-bus system demonstrate that model predictive damping controllers can effectively damp inter-area low frequency oscillation. They also have the ability to coordinate with power system stabilizers （PSSs） and other model predictive damping controllers in multi-machine power systems and the ability to adapt to changes in operation conditions.