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根据现场试验数据通过辨识算法辨识得到励磁系统模型参数是一种电力系统中广泛使用的励磁建模方法。基于辨识方法所得到的模型参数虽然可以很好地拟合系统的现场试验数据，但也可能存在某些参数的辨识结果不稳定的现象。因此，提出了拟频域灵敏度的概念，为非线性系统参数易辨识性的评估提供了一种新的衡量指标。并基于此提出了一种新的参数辨识算法，该方法通过构建参数时域灵敏度矩阵实现关联性参数的判断，将待辨识参数划分为良态参数集和病态参数集。在该初始病态参数集的基础上，根据参数的拟频域灵敏度重新调整参数赋值代表，采用“分轮次”的策略进行了参数辨识。以IEEE ST2A 型励磁系统为例，算例分析结果表明：与传统的基于时域灵敏度的辨识方法的辨识结果相比，所提出的参数辨识方法能够有效提高发电机励磁系统参数辨识结果的准确性和稳定性。“,”The method, which uses the experimental data to identify and obtain the parameters of the excitation system model, is widely used in the power systems. Although the model parameters obtained by parameter identification method can properly fit experimental data, the identification results of some parameters may be unstable. Therefore, this paper proposes a conception called sub-frequency domain sensitivity, which can provide a reliable index to assess whether the model parameters are easy to identify or not for a nonlinear system. Based on this conception, a new algorithm of parameter identification is proposed. In this algorithm, the existence of relevant parameters is judged by establishing the time domain sensitivity array of parameters at first, then the identified parameters are divided into two categories: well-conditioned and ill-conditioned parameters. Based on the original ill-parameter group, evaluation representatives of the parameters are readjusted according to the sub-frequency domain sensitivity of parameters, finally, a“divide and rule”strategy is used to identify parameters. Case study is undertaken based on the IEEE ST2A type excitation system. Analysis results reveal that the proposed method can improve the accuracy and stability of parameter identification results in comparison with the traditional identification method based on time domain sensitivity.