Abstract: A method for improving an autonomous microgrid, and an autonomous microgrid that includes a plurality of inverter-based distributed generations. Each of the inverter-based distributed generations is coupled to a corresponding power droop controller, a corresponding voltage controller, and a corresponding current controller. The autonomous microgrid further includes a constant power load (CPL) coupled to one of the plurality of inverted-based distributed generations. The CPL includes a phase locked loop (PLL), a DC voltage controller and an AC current controller. Power-sharing coefficients, controller parameters of the controllers and gains of the PLL are defined based on a weighted objective function that is calculated through on a particle swarm optimization.

Abstract: A method for improving an autonomous microgrid, and an autonomous microgrid that includes a plurality of inverter-based distributed generations. Each of the inverter-based distributed generations is coupled to a corresponding power droop controller, a corresponding voltage controller, and a corresponding current controller. The autonomous microgrid further includes a constant power load (CPL) coupled to one of the plurality of inverted-based distributed generations. The CPL includes a phase locked loop (PLL), a DC voltage controller and an AC current controller. Power-sharing coefficients, controller parameters of the controllers and gains of the PLL are defined based on a weighted objective function that is calculated through on a particle swarm optimization.

Abstract: The particle swarm optimization method for microgrids formulates a control problem as an optimization problem and PSO is used to search the solution space for optimal parameter settings in each mode. The procedure models optimal design of an LC filter, controller parameters and damping resistance in grid-connected mode. Moreover, the procedure optimizes controller parameters and power sharing coefficients in autonomous mode. The method uses particular nonlinear time-domain-based and eigenvalue-based objective functions to minimize the error in the measured power, and also to enhance the damping characteristics, respectively.

Abstract: The particle swarm optimization method for microgrids formulates a control problem as an optimization problem and PSO is used to search the solution space for optimal parameter settings in each mode. The procedure models optimal design of an LC filter, controller parameters and damping resistance in grid-connected mode. Moreover, the procedure optimizes controller parameters and power sharing coefficients in autonomous mode. The method uses particular nonlinear time-domain-based and eigenvalue-based objective functions to minimize the error in the measured power, and also to enhance the damping characteristics, respectively.