Abstract
Photovoltaic (PV) arrays and their electronic converters are subject to various environmental disturbances and component-related faults that affect their normal operations and result in a considerable energy loss. Therefore, it is ever demanding to design such closed-loop operating algorithms that tolerate faults, present acceptable performance, and avoid wear and tear in the systems. In this work, the core objective is to extract maximum power from a PV array subject to environmental disturbances and plant uncertainties. The system is considered under input channel uncertainties (i.e., faults) along with variable resistive load and charging stations. A neuro-fuzzy network (NFN)-based reference voltage is generated to extract maximum power while considering variable temperature and irradiance as inputs. Furthermore, the estimated reference is tracked by the actual PV voltage under two types of controllers: certainty-equivalence-based robust sliding mode (CERSMC) and certainty-equivalence-based robust integral sliding mode (CERISMC). These strategies benefit from improving the robustness against faults (disturbances). The proposed methods use the inductor current, which is recovered via the velocity observer and the flatness property of nonlinear systems. The system's stability is proven in the form of very appealing theorems. These claims are validated by the simulation results, which are carried out in a MATLAB environment.