REAL-TIME ASSESSMENT OF A SOLAR WATER PUMPING SYSTEM UNDER NORMAL AND FAULTY OPERATING CONDITIONS

Solar-powered water-pumping systems are indispensable for ensuring consistent, efficient crop irrigation in remote, off-grid regions. In this paper, an advanced control strategy is implemented using the adaptive neuro-fuzzy inference system (ANFIS) to ensure maximum power extraction under all weather conditions. In addition, the induction motor (whether healthy or with one or more broken rotor bars) is coupled to a centrifugal pump in the solar water pumping system (SWPS). The motor's performance is precisely controlled using direct torque control (DTC). Furthermore, a fast Fourier transform (FFT) is used to monitor the induction motor’s state in the SWPS. Finally, the developed SWPS, operating under both healthy and faulty conditions, is experimentally validated via hardware-in-the-loop (HIL) tests using the OPAL-RT OP5600 platform and the Virtex-6 ML605 FPGA. Thanks to the applied methods, the investigated SWPS exhibits highly satisfactory, reliable performance across varying operating conditions.