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This paper proposes an online data-driven Koopman-inspired identification andcontrol method for microgrid secondary voltage and frequency control. Unliketypical data-driven methods, the proposed method requires no warm-up trainingyet with guaranteed bounded-input-bounded-output (BIBO) stability and evenasymptotic stability under some mild conditions. The proposed method estimatesthe Koopman state space model adaptively so as to perform effective secondaryvoltage and frequency control that can handle microgrid nonlinearity anduncertainty. Case studies in the 4-bus and 13-bus microgrid test systems (withgrid-forming and grid-following sources) demonstrate the effectiveness androbustness of the proposed identification and control method subject to thechange of operating conditions and large disturbances (e.g., microgrid modetransitions, generation/load variations) even with measurement noises and timedelays.

A Novel Koopman-Inspired Method for the Secondary Control of Microgrids with Grid-Forming and Grid-Following Sources