Model-based fault detection of hybrid fuel cell and photovoltaic direct current power sources

Hybrid DC power sources which consist of fuel cells, photovoltaic and lithium-ion batteries provide clean, high efficiency power supply. This hybrid DC power sources can be used in many applications. In this work, a model-based fault detection methodology for this hybrid DC power sources is presented. Firstly, the dynamic models of fuel cells, photovoltaic and lithium-ion batteries are built. The state space model of hybrid DC power sources is obtained by linearizing these dynamic models in operation points. Based on this state space model the fault detection methodology is proposed. Simulation results show that model-based fault detection methodology can find the fault on line, improve the generation time and avoid permanent damage to the equipment.

Research highlights
► The state space models of fuel cells, photovoltaic and lithium-ion batteries are built.
► The fault detection methodology based on state space model is used in hybrid DC power source.
► Model-based fault detection finds the hybrid DC power sources fault on line.
► Model-based fault detection improves the generation time and avoids permanent damage to the equipment.