The impedance-based method is favored by engineering because it can analyze system stability under conditions with the unknown device control structure or parameters. Considering that the impedance characteristics of AC grid-connected equipment represented by power electronic converters are easily affected by the AC steady-state operating point

quickly deriving an impedance model for any operating condition of the converter using black-box identification can greatly improve the efficiency of stability analysis. The neural network-based can eliminate the limitations of the least squares method-based identification

this paper further improves the neural network design to significantly improve its interpretability. In the data collection stage

the frequency sweep method is used to obtain the frequency response of the closed-loop impedance model under enough operating conditions. In the model training stage

taking into account the latent features of the converter impedance model

a neural network with the same number as the disturbance frequency was designed

and the Levenberg-Marquardt algorithm with Bayesian regularization integrated is used to enhance the generalization ability of the network trained with a small dataset. In the model verification phase

the network is fed with set operating conditions

achieving highly accurate identification of stable operating conditions and offline prediction.