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为提高发电机动态参数辨识的准确性，提出一种基于电网动态特性的发电机主导参数辨识方法。首先，根据实测量对发电机进行解耦，构建了包含动态参数的同步发电机的离散非线性状态空间模型，据此给出了参数核平滑算法(kernel smoothing，KS)和祖先采样粒子吉布斯算法(particle Gibbs with ancestor sampling，PGAS)，并将两种算法结合得到KS-PGAS算法；然后，基于Morris筛选方法，使用灵敏度因子判别动态参数变化对输出的影响程度，并筛选出第一摆最大功率与阻尼比两个特征量相对应的主导参数；最后，使用KS-PGAS算法先对第一摆最大功率对应的主导参数修正，再利用修正后的主导参数对阻尼比对应的主导参数进行修正。仿真结果证明了所提算法的有效性和优越性。“,”To improve the accuracy of dynamic parameters of generator, a new methodology for generator dominant parameter identification based on dynamic characteristics of power grid was presented. Firstly, the decoupling decomposition of the generator was carried out according to the real measurement. The nonlinear discrete state space model of the synchronous generator was constructed which contains dynamic parameters. In addition, Kernel smoothing (KS) and Gibbs with ancestor sampling particle (PGAS) were given, and the two algorithms were combined to obtain the KS-PGAS algorithm. Furthermore, the influence degree of the dynamic parameter variation based on the Morris filter method was selected by using the sensitivity factors. The dominant parameters were selected by the two characteristic parameters which were the maximum amplitude and the damping ratio. Finally, using the KS-PGAS algorithm, the dominant parameters of the maximum amplitude were identified, and then the dominant parameters of the damping ratio were identified based on the first identified parameter. Simulation results demonstrate the effectiveness and superiority of the proposed algorithm.