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

Abstract

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.