Iterative Learning-Based Distributed Secondary Control for AC Microgrid

Abstract

Existing distributed secondary control strategies relying on the microgrid system model have good theoretical performance but are difficult to implement in practice due to the need for real-time observation of numerous system states. This article addresses this issue by proposing a distributed secondary control strategy for ac microgrids based on ILC. During operation, this strategy neither relies on the microgrid system model nor imposes any computational burden. First, this article introduces the microgrid hierarchical control structure and establishes a system model for primary control. Then, the proposed ILC-based secondary control strategy is elaborated, and the corresponding stability criterion is derived using the small-signal analysis method. Finally, the simulation results in MATLAB/Simulink demonstrate the effectiveness of the proposed ILC-based strategy. Specifically, the proposed strategy can limit the transient time within 0.4 s when handling various disturbances, which is comparable to the existing strategies based on MPC and SMC. However, the proposed strategy is easier to implement compared with the MPC-based and SMC-based strategies, which rely on the system model. Furthermore, in plug-and-play and load change scenarios, the proposed control strategy can suppress voltage fluctuation within 1 V, which is better than existing strategies.