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 system for efficient power management control includes a microgrid, a primary electrical grid, a first control unit, a second control unit, and a third control unit. The microgrid, which is an active load based microgrid, is electrically coupled with the primary electrical grid in grid-connected mode and includes at least one photovoltaic (PV) array, at least one battery, and at least one generator. The first control unit is used to manage the at least one PV array. The second control unit is used to manage the at least one battery. The third control unit is used to control the at least one generator. In grid-connected mode, a system frequency and voltage supplied to the active load is regulated by the primary electrical grid. During a fault in island mode, the at least one generator controls the system frequency and voltage.

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 fault ride-through and power smoothing system includes a buck converter for Maximum Power Point Tracking (MPPT) of a PV array power system, a buck-boost converter to connect a supercapacitor energy storage system (SCESS) to the DC link, and a voltage source converter (VSC) to transfer the DC link power to the grid. Three independent controllers are implemented, one for each power electronics block. The effectiveness of the controllers is examined on Real Time Digital Simulator (RTDS).

Abstract: The neuro-fuzzy control system for a grid-connected photovoltaic (PV) system includes an Adaptive Neuro-Fuzzy Inference System (ANFIS) implemented in real time. Independent active and reactive P-Q power control transfers the generated power to the grid using a voltage source inverter (VSI). The PV system includes a PV module, a buck converter, a VSI, a maximum power point tracking (MPPT) controller for the buck converter, and a VSI controller. The MPPT controller uses irradiation and temperature as inputs. A five-layer ANFIS processes these inputs and provides a control reference voltage as input to a PI controller connected to the buck converter to maintain the output voltage of the photovoltaic array with respect to the control reference voltage.

Abstract: The fault ride-through and power smoothing system includes a buck converter for Maximum Power Point Tracking (MPPT) of a PV array power system, a buck-boost converter to connect a supercapacitor energy storage system (SCESS) to the DC link, and a voltage source converter (VSC) to transfer the DC link power to the grid. Three independent controllers are implemented, one for each power electronics block. The effectiveness of the controllers is examined on Real Time Digital Simulator (RTDS).

Abstract: The neuro-fuzzy control system for a grid-connected photovoltaic (PV) system includes an Adaptive Neuro-Fuzzy Inference System (ANFIS) implemented in real time. Independent active and reactive P-Q power control transfers the generated power to the grid using a voltage source inverter (VSI). The PV system includes a PV module, a buck converter, a VSI, a maximum power point tracking (MPPT) controller for the buck converter, and a VSI controller. The MPPT controller uses irradiation and temperature as inputs. A five-layer ANFIS processes these inputs and provides a control reference voltage as input to a PI controller connected to the buck converter to maintain the output voltage of the photovoltaic array with respect to the control reference voltage.