Time-Domain Modeling and Simulation of Hybrid Perturb and Observe–Particle Swarm Optimization Maximum Power Point Tracking for Enhanced CubeSat Photovoltaic Energy Harvesting

Khaya Ntutuzelo Dwaza (), Senthil Krishnamurthy and Haltor Mataifa
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Khaya Ntutuzelo Dwaza: Department of Electrical Engineering, School of Engineering, Faculty of Engineering, Built Environment and Information Technology, Walter Sisulu University, Buffalo City Campus, PO Box 1421, East London 5200, Eastern Cape, South Africa
Senthil Krishnamurthy: Center for Intelligent Systems and Emerging Technologies, Department of Electrical, Electronic and Computer Engineering, Cape Peninsula University of Technology, Bellville, Symphony Way, Bellville, P.O. Box 1906, Cape Town 7535, South Africa
Haltor Mataifa: Center for Intelligent Systems and Emerging Technologies, Department of Electrical, Electronic and Computer Engineering, Cape Peninsula University of Technology, Bellville, Symphony Way, Bellville, P.O. Box 1906, Cape Town 7535, South Africa

Energies, 2025, vol. 18, issue 22, 1-28

Abstract: The escalating demand for efficient energy harvesting in CubeSat missions necessitates advanced maximum power point tracking (MPPT) techniques. This work presents a comprehensive time-domain analysis and simulation of three MPPT algorithms: perturb and observe (PO), particle swarm optimization (PSO), and a novel hybrid PO-PSO method, tailored explicitly for CubeSat photovoltaic (PV) solar modules. Utilizing MATLAB R2025a/Simulink, a detailed model of a PV module based on the Azur Space 3G30C datasheet and a DC-DC boost converter was developed. The conventional PO MPPT, while simple, demonstrated limitations in tracking the global maximum power point (GMPP) under rapidly changing temperature conditions and exhibited significant oscillations around the GMPP. The PSO algorithm, known for its global search capabilities, was investigated to mitigate these shortcomings. This research introduces a hybrid PO-PSO MPPT technique that synergistically combines the low computational complexity of PO with the robust global optimization of PSO. Time-domain simulation results demonstrate that the proposed hybrid PO-PSO MPPT significantly reduces oscillations around the GMPP, enhances tracking accuracy under varying temperature conditions, and stabilizes output parameters more effectively than standalone PO or PSO methods. These findings validate the hybrid approach as a superior and reliable solution for optimizing power generation in constrained CubeSat applications.

Keywords: maximum power point tracking (MPPT); perturb and observe (PO); particle swarm optimization (PSO); cube satellite (CubeSat); photovoltaic (PV); time-domain simulation; energy harvesting; global maximum power point (GMPP); MATLAB/Simulink (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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