An Adaptive Strategy Based on Repetitive Predictive Control for Improving Adaptability of <i>LCL-</i>Type Grid-Connected Inverters Under Weak Grid
Mingming Li, Huafeng Xiao, Ming Cheng
Abstract
For current-controlled grid-connected inverters with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LCL</i> filter, the essence of grid voltage full feedback is to improve the output impedance of grid-connected inverters to infinity, which can eliminate the influences of grid voltage distortion and the non-negligible grid impedance. However, existing full feedforward strategies are complex and control delay cannot be compensated precisely, which leads to the instability of system when the full feedforward of the voltage at the point of common coupling under the weak grid is adopted. This article first proposes a simplified full feedforward strategy based on grid-side inductor volt-age differential feedback active damping (GIVD-AD). Second, repetitive prediction (RP) is used to compensate for the control delay. Then, a virtual impedance branch is added and an adaptive algorithm for modifying virtual impedance based on the difference between predicted grid voltages by Newton interpolation prediction (NIP) and RP is proposed. With the proposed method, grid-connected inverters can work stably when grid impedance changes suddenly and exhibit strong rejection ability against grid-voltage harmonics. Finally, simulative and experimental results from a 3-kW single-phase grid-connected inverter are provided to prove the effectiveness of the proposed strategy.