Modelling and simulation of the cube satellite power systems

Abstract

The power availability for CubeSat missions critically depends on the efficiency of solar panel power generation, control, and regulation, particularly given the constraints of fitting within a ten cm³ volume. This thesis focuses on optimizing solar power generation using Maximum Power Point Tracking (MPPT) techniques to maximize the utility of the limited solar panel area available on a CubeSat. A comprehensive comparative analysis of conventional MPPT methods is presented, specifically focusing on the Perturb and Observe (PO) technique chosen for its low computational complexity. The PO MPPT technique was implemented using a DC-DC boost converter and a PV module based on the Azur Space 3G30C datasheet in MATLAB/Simulink. During the implementation, it was observed that without PO MPPT control, the output current, voltage, and power exhibited significant ripple between minimum and maximum levels. With the application of PO MPPT, these outputs stabilized; however, the technique was found to have significant limitations. A critical research gap identified was PO's poor tracking of the Maximum Power Point (MPP) under fast-changing meteorological conditions, coupled with pronounced oscillations around the Global Maximum Power Point (GMPP). The thesis explores advanced MPPT techniques, including a varying step-size PO method, to address these issues, as existing literature suggests. Additionally, the potential of Artificial Intelligence (AI) algorithms—such as Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Genetic Algorithms (GA)—to enhance PO MPPT performance under varying conditions was investigated. These AI-driven approaches have shown promise in reducing oscillations and improving tracking accuracy at the GMPP. This research introduces a novel hybrid PO-PSO MPPT technique, which combines the simplicity of PO with the global search capability of PSO. Simulation results demonstrated that the hybrid PO-PSO MPPT method significantly mitigates the oscillations at the GMPP, enhances tracking under varying temperature conditions, and stabilizes the output parameters more effectively than conventional methods, including GA-tuned PID controllers and standalone PSO MPPT functions. These findings validate the hybrid PO-PSO approach as a superior solution for optimizing power generation in CubeSat applications, addressing the identified research gaps and providing a robust framework for future small satellite power systems. Keywords: Maximum Power Point Tracking (MPPT); Perturb and Observe (PO); Maximum Power Point (MPP); Photo Voltaic (PV); Cube Satellite (CubeSat); Pulse Width Modulation (PWM); Proportional Integral derivative (PID) controller; Particle Swarm Optimisation (PSO); integral square error (ISE); integral time absolute error (ITAE); Integral absolute error (IAE); Integral time square error (ITSE); artificial intelligence (AI); global maximum power point (GMPP).