  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.
