Abstract
This study compares the performances of Meta-heuristic grey wolf optimization (GWO) and analytical-based Perturb and Observe (P&O) algorithms to extract maximum power from a photovoltaic system exposed to rapid variation of sunlight and partial shading conditions. Theoretical models were created using the Pvlib library, a new and flexible module in the Python environment. Algorithm performances have been evaluated on the DC-DC step-down converter built using the PySpice/Python library. The results show that the GWO algorithm provides superior tracking performance in different weather conditions than P&O.
Keywords
Python (programming language)
Photovoltaic system
Shading
Maximum power point tracking
Meta heuristic
Computer science
Algorithm
Engineering
Electrical engineering
Computer graphics (images)
Voltage
Subject Areas
Photovoltaic System Optimization Techniques
·Renewable Energy, Sustainability and the Environment ·Physical Sciences
Solar Radiation and Photovoltaics
·Artificial Intelligence ·Physical Sciences
solar cell performance optimization
·Electrical and Electronic Engineering ·Physical Sciences
Citations by Year
2024: 2
2025: 3
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