Data-Driven Fuzzy Logic Approach for Input Admittance Estimation in Grid-Tied Inverters

Information about the input admittance of a three-phase grid connected inverter is important in stability assessment when connected to a power grid. However, existing neural network (NN)-based estimation methods typically require extensive training data and depend on frequency sweep as an additional input to generalize the estimation. This paper proposes a data-driven fuzzy logic system (FLS) for input admittance estimation of a three-phase grid-connected inverter, achieving generalization using only the system's operating points (OPs) as inputs, without requiring frequency information. By defining a limited set of rule bases, the transfer function (TF) coefficients of the input admittance are deduced as the FLS output, without requirement on extensive training data. Simulation results have confirmed the efficiency of the proposed method.