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Enhancing solar water pumping systems: A machine learning approach to investigating hybrid converters

Author

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  • Sumathi, S.
  • Selvarajan, L.

Abstract

This research explores the use of a CUK converter and a Single Ended Primary Inductor Converter (SEPIC) in a solar water pumping system with Maximum Power Point Tracking (MPPT). The system drives a Brushless DC (BLDC) motor via centrifugal force on the shaft. MPPT, utilizing the Gravitational Search Algorithm (GSA) and Modified Particle Swarm Optimization (MPSO), ensures efficient control of the converter and smooth motor start-up with zero ripple current for hybridization. The converter minimizes supply current ripple and improves photovoltaic power efficiency by combining input and output magnetic cores of an inductance. This study addresses the integration of solar energy with water resources, particularly for India's smart irrigation systems. The performance of the BLDC motor with a centrifugal pump, powered by solar energy, was analyzed under both static and transient conditions using CUK-SEPIC, with results validated through MATLAB/Simulink. SEPIC's ability to provide a stable output voltage, minimal noise, and low component count makes it suitable for solar water pumping. A 5.0 kW prototype model of the PV power conditioning device, utilizing the GSA-MPSO approach, achieved a high settling time of 0.5 s for 5.0 kW output, significantly enhancing power efficiency.

Suggested Citation

  • Sumathi, S. & Selvarajan, L., 2025. "Enhancing solar water pumping systems: A machine learning approach to investigating hybrid converters," Renewable Energy, Elsevier, vol. 250(C).
    • Handle: RePEc:eee:renene:v:250:y:2025:i:c:s0960148125009437
    DOI: 10.1016/j.renene.2025.123281
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