针对固定数据窗口难以适应时变频率波动的问题，提出基于可变数据窗口的电力系统惯量估计方法。首先，分析电力系统惯量响应过程，利用有源自回归（auto-regressive with extra inputs，ARX）模型表征频率动态特性，建立参数辨识模型。其次，基于自适应中值-均值组合滤波（adaptive median-mean combined filter，AMMCF）方法进行数据预处理，并采用模糊逻辑控制器（fuzzy logic controller，FLC）设置带遗忘因子递推最小二乘（forgetting factor recursive least square，FF-RLS）方法的参数。然后，构建量化辨识误差以及连续窗口间辨识结果差异程度的评估指标，分场景在线调整数据窗口长度，以估计系统惯量。最后，通过算例分析验证了所提方法的有效性。算例结果表明：所提方法能够适应惯量参数变化的场景，有效降低惯量估计误差。

Due to the ineffectiveness of the fixed data window caused by time-varying frequency dynamics and inertia, an online estimation for inertia of power system based on variable data window is proposed. First, the process of power system inertia response is analyzed, and the auto-regressive with extra inputs (ARX) model is used to depict frequency dynamics, on which a parameter identification model is built. Then, based on the adaptive median-mean combined filter (AMMCF), the frequency measurement data are preprocessed. And the fuzzy logic controller (FLC) is used to set the parameters of the forgetting factor recursive least square (FF-RLS) method. Moreover, the indices measuring the identification error and the difference of the results of successive windows are established to realize the online variation of data window length and calculate the system inertia. Finally, case studies are carried out to validate the effectiveness of the proposed approach. The case study results show that the proposed approach can adapt to the changing inertia and is effective to reduce the inertia estimation error.