Two-loop controller for maximizing performance of a grid-connected photovoltaic-fuel cell hybrid power plant

Maximizing performance of a grid-connected photovoltaic (PV)-fuel cell hybrid system by use of a two-loop controller is discussed. One loop is a neural network controller for maximum power point tracking, which extracts maximum available solar power from PV arrays under varying conditions of insolation, temperature, and system load. A real/reactive power controller (RRPC) is the other loop. The RRPC achieves the system´s requirements for real and reactive powers by controlling incoming fuel to fuel cell stacks as well as switching control signals to a power conditioning subsystem. Results of time-domain simulations prove not only the effectiveness of the proposed computer models of the two-loop controller but also its applicability for use in stability analysis of the hybrid power plant