A novel method for solar water pumping system using machine learning techniques Available to Purchase

This research article presents with the application of a Cuk converter and Single Ended Primary Inductor Converter (SEPIC) for solar water pumping with Maximum Power Point Tracking (MPPT). A centrifugal pump linked to its shaft is driven by a permanent magnet brushless DC (BLDC) motor. The Incremental Conductance Maximum Power Point Tracking (INC-MPPT) technique is used to manage the converter and soft start the BLDC motor with zero ripple current. A hybridized MPPT technique based on Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO) is presented. This new converter lowers supply current ripple to a minimum and maximum the amount of Photovoltaic power collected from a solar component by merging the input and output magnetic cores of an inductance. The proposed method is more efficient since the motor speed remains constant even when the load is full. The dynamic and steady-state performance of a BLDC motor coupled to a centrifugal water pump fed by the SPV array-SEPIC is assessed, and its applicability is confirmed using simulated results in the MATLAB/ Simulink environment. The experimental outcomes for a 5.0 kW prototype system are discussed for a PV power conditioning system. The CUK-SEPIC achieves high settling time of 0.9 for a 5.0 kW output power, improving power efficiency. The performance of proposed system is compared with different techniques.