Few‐shot learning based multi‐weather‐condition impedance identification for MPPT‐controlled PV converters

The broadband impedance of converters is an essential feature for the stability analysis of new energy sources. However, obtaining the impedance for photovoltaic (PV) converters with Maximum Power Point Tracking (MPPT) control is challenging because of their non-linear control schemes and real-time changing operating points. In this case, conventional linearized impedance modelling methods are not applicable, and conventional direct measurement approaches are highly time-consuming. This paper proposes a few-shot learning approach for quick access to PV converters' impedance. The proposed method is based on the model agnostic meta-learning (MAML) algorithm, suitable for MPPT-controlled converters whose impedance changes with time, temperature, and irradiation. In the training process, it adjusts the initial model of the machine learning algorithm under different weather conditions. After completing the training, the initial model can adapt to a new condition with very few samples. Under this approach, with only a few data measured at several frequency points, broadband impedance for MPPT-controlled converters under any weather conditions can be accurately predicted, avoiding time-consuming measurements and inaccurate prediction of existing methods. Contrast simulation results show the effectiveness and superiority of the proposed method.