Design of a Novel Chaotic Horse Herd Optimizer and Application to MPPT for Optimal Performance of Stand-Alone Solar PV Water Pumping Systems

Rabeh Abbassi () and Salem Saidi
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Rabeh Abbassi: Department of Electrical Engineering, College of Engineering, University of Ha’il, Ha’il City 81451, Saudi Arabia
Salem Saidi: LaTICE Laboratory, Higher National Engineering School of Tunis (ENSIT), University of Tunis, 5 Avenue Taha Hussein, P.O. Box 56, Tunis 1008, Tunisia

Mathematics, 2024, vol. 12, issue 4, 1-26

Abstract: A significant part of agricultural farms in the Kingdom of Saudi Arabia (KSA) are in off-grid sites where there is a lack of sufficient water supply despite its availability from groundwater resources in several regions of the country. Since abundant agricultural production is mainly dependent on water, farmers are forced to pump water using diesel generators. This investigation deals with the increase in the effectiveness of a solar photovoltaic water pumping system (SPVWPS). It investigated, from a distinct perspective, the nonlinear behavior of photovoltaic modules that affects the induction motor-pump because of the repeated transitions between the current and the voltage. A new chaotic Horse Herd Optimization (CHHO)-based Maximum Power Point Tracking technique (MPPT) is proposed. This algorithm integrates the capabilities of chaotic search methods to solve the model with a boost converter to maximize power harvest while managing the nonlinear and unpredictable dynamical loads. The analytical modeling for the proposed SPVWPS components and the implemented control strategies of the optimal duty cycle of the DC–DC chopper duty cycle and the Direct Torque Control (DTC) of the Induction Motor (IM) has been conducted. Otherwise, the discussions and evaluations of the proposed model performance in guaranteeing the maximum water flow rate and the operation at MPP of the SPVWPS under partial shading conditions (PSC) and changing weather conditions have been carried out. A comparative study with competitive algorithms was conducted, and the proposed control system’s accuracy and its significant appropriateness to improve the tracking ability for SPVWPS application have been proven in steady and dynamic operating climates and PSC conditions.

Keywords: metaheuristic optimization; artificial intelligence; solar photovoltaic water pumping; Saudi Vision 2030; smart irrigation systems; power reliability and energy efficiency; maximum power point tracking; sustainable agriculture (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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