基于状态空间模型与PEM迭代算法的电力系统惯量辨识

惯量是保障电力系统频率安全稳定的重要参数之一，因此需对其进行精确的在线测量。针对当前采用的系统辨识方法测量精度不高的问题，研究系统辨识中算法模型的选择对测量结果的影响。首先，分析比较现有的传递函数模型、自回归滑动平均模型以及子空间辨识模型进行惯量辨识的测量原理。其次，从惯量响应初期阶段数据匹配的角度，提出基于PEM迭代算法的状态空间估计模型。最后，搭建10机39节点电力仿真系统，验证了所提辨识模型的正确性。并在不同功率扰动程度以及不同采样时间窗口下，分析4种辨识模型的适用性，为确定系统最优辨识模型提供参考依据。

Power system inertia identification based on a state space model and a PEM iterative algorithm

Inertia is one of the important parameters which can ensure that a power system frequency is in a stable state. So it needs to be measured accurately online. Aiming at the low measurement accuracy of the current system identification method, the influence of the algorithm model in system identification on the measurement result is studied. First, the principles of inertia identification based on transfer function model, auto-regressive moving average model and subspace identification model are analyzed and compared. Secondly, a state space estimation model based on a PEM iterative algorithm is proposed from the perspective of data matching at the initial stage of the inertia response. Finally, a 10-machine and 39-bus power simulation system is built to verify the correctness of the proposed identification model. The applicability of the four identification models is analyzed under different power disturbance degrees and sampling time windows. It provides a reference for power system operators to determine the optimal identification model. This work is supported by the National Natural Science Foundation of China (No. 51977128).