Iterative Learning-Based Power Distribution Control of Multisource Inverters Via Zero-Sequence Component Compensation

The multisource inverter (MSI) emerges as a promising solution for hybrid energy sources-connected islanded microgrids due to its advanced merits of single-stage power conversion without employing dc–dc converters. However, the conventional linear controller-based power distribution schemes suffer from excessive port power ripples because the zero-sequence component employed as control input presents uncertain switching features. Given this, this article proposes an iterative learning-based power distribution control (ILPDC) scheme to achieve ripple-free port power regulation via zero-sequence component compensation. In the proposed ILPDC, a feedforward-feedback structure with a dedicated iterative learning term is designed to be capable of extracting uncertain switching features from the stored error information. Specifically, the feedback control term forces the port power to approach the reference, and the iterative learning term provides a zero-sequence component compensation mechanism to eliminate the final error induced by the switching features. Besides, the convergence of the proposed scheme is analyzed theoretically. With the proposed scheme, the desired power distribution of MSI can be achieved with reduced port power ripples. Finally, experimental results verify the effectiveness and performance of the proposed scheme.