Parameter identification facilitates the improvement of the accuracy of power system simulation. However, for complex models of electrical equipment, it is impossible to simultaneously identify all the parameters. Inaccurate values of the nontarget parameters (NTPs) will affect the identification accuracy of the target parameters (TPs). However, no suitable methods are available in the parameter identification process to assess and handle this adverse impact. Therefore, a probabilistic preassessment method (PPM) is proposed to assess the possible identification accuracy of the TPs when the NTPs are inaccurate. PPM can provide more useful quantitative information than traditional sensitivity analysis for selecting the disturbance form, the observation variable, and the TPs that can be accurately identified. Then, a statistical identification process (SIP) is proposed to eliminate the dependence of the traditional parameter identification method on accurate NTPs. In SIP, parameter identification is repeated by using random values of the NTPs. Then, the mean value of the high-probability identification results is selected as the final result. Some of the identification results can be adjusted according to the PPM result to further improve the accuracy. The proposed methods were successfully used to identify the parameters of a two-mass drive train model of a DFIG wind turbine generator under the assumption that the generator and controller parameters are unknown.

中文翻译：

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参数辨识精度的概率预评估方法在双馈电机传动系统参数辨识中的应用

参数辨识有利于提高电力系统仿真精度。然而，对于复杂模型的电气设备，不可能同时识别所有参数。非目标参数 (NTP) 的不准确值会影响目标参数 (TP) 的识别准确性。然而，在参数识别过程中没有合适的方法来评估和处理这种不利影响。因此，提出了一种概率预评估方法（PPM）来评估当 NTP 不准确时可能识别 TP 的准确性。PPM 可以提供比传统灵敏度分析更有用的定量信息，用于选择扰动形式、观测变量和可准确识别的 TP。然后，提出了统计识别过程（SIP）以消除传统参数识别方法对准确NTP的依赖。在 SIP 中，通过使用 NTP 的随机值来重复参数识别。然后，选择高概率识别结果的平均值作为最终结果。部分识别结果可以根据PPM结果进行调整，进一步提高精度。在发电机和控制器参数未知的假设下，所提出的方法成功地用于识别双馈风力涡轮发电机的双质量传动系统模型的参数。然后，选择高概率识别结果的平均值作为最终结果。部分识别结果可以根据PPM结果进行调整，进一步提高精度。在发电机和控制器参数未知的假设下，所提出的方法成功地用于识别双馈风力涡轮发电机的双质量传动系统模型的参数。然后，选择高概率识别结果的平均值作为最终结果。部分识别结果可以根据PPM结果进行调整，进一步提高精度。在发电机和控制器参数未知的假设下，所提出的方法成功地用于识别双馈风力涡轮发电机的双质量传动系统模型的参数。