  In this paper, a biologically-inspired adaptive intelligent secondary
controller is developed for microgrids to tackle system dynamics uncertainties,
faults, and/or disturbances. The developed adaptive biologically-inspired
controller adopts a novel computational model of emotional learning in
mammalian limbic system. The learning capability of the proposed
biologically-inspired intelligent controller makes it a promising approach to
deal with the power system non-linear and volatile dynamics without increasing
the controller complexity, and maintain the voltage and frequency stabilities
by using an efficient reference tracking mechanism. The performance of the
proposed intelligent secondary controller is validated in terms of the voltage
and frequency absolute errors in the simulated microgrid. Simulation results
highlight the efficiency and robustness of the proposed intelligent controller
under the fault conditions and different system uncertainties compared to other
benchmark controllers.
