A Hybrid Particle Swarm Optimization Algorithm for Maximum Power Point Tracking of Solar Photovoltaic Systems

Nicholas Foster, Brian McCray, Samuel McWhorter


In this paper, we investigated a maximum power point tracking (MPPT) technique based on a hybrid particle swarm optimization (PSO) algorithm to harvest maximum energy from a solar photovoltaic (PV) module. The hybrid PSO algorithm was designed to search and locate the maximum power point (MPP) on the power-voltage curve for abrupt changes in solar irradiance, and automatically switch to perturb and observe (P&O) mode to keep track of the MPP under steady state operation. The designed hardware consisted of a buck-boost converter operating at 100 kHz. 16-bit analog-to-digital (ADC) converters were used to accurately measure the voltages and currents at the load side, which was then read by an Arduino microcontroller to implement the hybrid PSO algorithm. The MPPT algorithm was executed on the microcontroller, which controls the MOSFET switches of the power converter by varying the duty cycle to achieve MPP tracking. MATLAB simulations were performed to find the maximum power and the corresponding optimum duty cycle under various irradiance conditions. Simulated results were verified by experiments using a 40 watt solar module and the experimental results closely agree with the values obtained by simulation. We are currently investigating the effects of several key operating parameters of the PSO technique, such as the particle count, PSO search space, and processor clock speed to further optimize the algorithm and achieve highest possible efficiency.  The detailed description of the developed algorithm, the MATLAB simulation, and design of the experimental hardware platform with the results are presented here.


Maximum Power Point Tracking; PSO; Photovoltaic; Solar Power; Renewable Energy

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