Performance Evaluation of Four Meta-Heuristic MPPT Algorithms for a DC-DC Converter based PV System under Dynamic Weather Conditions (DWC)

Abstract

Solar Photovoltaic (PV) Systems are one of the most favorable and advantageous power generation frameworks used worldwide to fulfill the global requirements/needs for Renewable & Clean Energy. The output power characteristics generated by the PV Systems are non-linear and are strongly dependent upon the variable environmental parameters such as the Solar Irradiation (W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) and Temperature (°C). Each PV System has its own ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$V_{MPP},I_{MPP}$</tex> ) point defined at a specific Irradiation (W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) and Temperature (°C) value at which the power is optimal. Thus, in order to ensure the continuous operation of the PV System at the Maximum Power-Point (MPP), an intelligently smart Maximum Power-Point Tracking (MPPT) mechanism is mandatorily implemented along with the Photovoltaic (PV) System. This paper exhibits a comparative study on the behaviors of the four MPPT algorithms namely: Cuckoo Search Algorithm (CSA), Flower Pollination Algorithm (FPA), Grey Wolf Optimization Algorithm (GWOA), and the Particle Swarm Optimization Algorithm (PSOA) under Dynamic Weather/Environmental conditions based on fluctuations in Temperature (°C) and Solar Irradiation (W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) for a DC-DC Step-Up Converter based Photovoltaic (PV) System that is modeled & implemented under Matlab/Simulink Environment.