Three‐Phase PV Pumping System With Advanced Control for Enhanced Efficiency and Robustness: Modeling, Experimental Validation, and Optimization

ABSTRACT This paper presents a detailed study of a three‐phase photovoltaic pumping system (PVPS), comprising a 1.5‐kW three‐phase induction motor for water pumping, a three‐phase voltage source inverter (VSI), and a DC‐DC boost converter designed to maximize power extraction from a 1.88‐kWp photovoltaic generator (PVG). The system is characterized by its cost‐effectiveness, high efficiency, and robust performance. To reduce overall system costs, the inductor current of the boost converter and the rotational speed of the induction motor are estimated rather than measured directly. A nonlinear neural network observer (NNO) is employed to estimate the inductor current, whereas a sliding mode observer (SMO) is utilized to estimate the motor speed. To enhance the system's resilience against internal and external disturbances, a hybrid incremental conductance super‐twisting sliding mode controller (InC‐STSMC) is implemented for maximum power point tracking (MPPT) from the PVG, and a flux‐oriented sliding mode vector control (FO‐SMC) is adopted for precise regulation of the motor speed. The effectiveness of the proposed control strategy is evaluated through model‐in‐the‐loop (MIL) simulations conducted in the MATLAB‐Simulink environment, demonstrating significant improvements in dynamic performance, particularly in terms of stability and robustness, compared to conventional proportional‐integral (PI) control methods. The practicality and suitability of the proposed InC‐STSMC combined with the NNO scheme are further validated through a processor‐in‐the‐loop (PIL) test using the STM32F769I board, highlighting its potential for real‐world applications.