A Novel Koopman-Inspired Method for the Secondary Control of Microgrids with Grid-Forming and Grid-Following Sources
Xun Gong, Xiaozhe Wang
TL;DR
This work tackles secondary voltage and frequency control in microgrids with mixed grid-forming and grid-following sources under large disturbances. It introduces a data-driven Koopman-inspired framework that online-identifies a linear Koopman state space via an enhanced OKID and applies discrete-time LQR in the Koopman coordinates, using observables that capture key nonlinear interactions. The method requires no offline training and guarantees $BIBO$ stability, with conditions for asymptotic stability and robustness to measurement noise and delays. Case studies on 4-bus and 13-bus MGs demonstrate improved restoration performance, demand-uncertainty resilience, and scalability without relying on detailed network models or prior controller parameters.
Abstract
This paper proposes an online data-driven Koopman-inspired identification and control method for microgrid secondary voltage and frequency control. Unlike typical data-driven methods, the proposed method requires no warm-up training yet with guaranteed bounded-input-bounded-output (BIBO) stability and even asymptotic stability under some mild conditions. The proposed method estimates the Koopman state space model adaptively so as to perform effective secondary voltage and frequency control that can handle microgrid nonlinearity and uncertainty. Case studies in the 4-bus and 13-bus microgrid test systems (with grid-forming and grid-following sources) demonstrate the effectiveness and robustness of the proposed identification and control method subject to the change of operating conditions and large disturbances (e.g., microgrid mode transitions, generation/load variations) even with measurement noises and time delays.