Abstract: The data-driven neural network modeling approach has been widely used for analyzing the multi- operating condition impedance/admittance models of power electronic devices. However, there are limited samples of admittance data obtained from actual measurements, and the quality of impedance data is compromised due to the influence of measurement noise. This degradation in data quality can negatively impact the predictive performance of the model, resulting in significant errors between the model's predictions and the actual admittance. To address this issue, this paper proposes a data-driven approach for obtaining multi-operating condition admittance models that consider the effects of measurement errors. Initially, the guidance for neural network training is based on the mean square error (MSE) as the evaluation metric between model predictions and actual values. Then, the paper analyzes the impact of voltage and current noise under various operating conditions on admittance measurements and establishes the relationship between measurement errors and above metrics. Furthermore, Bayesian algorithms are employed to search for optimal model parameters that minimize the metrics, thereby reducing the interference of noise samples in neural network model training and improving the accuracy of model outputs. Finally, this paper constructs a back propagation neural network (BPNN) admittance model based on the doubly fed induction generator (DFIG) and validates the proposed method with a dataset containing measurement errors.