Data of simulation model for photovoltaic system's maximum power point tracking using sequential Monte Carlo algorithm

Alhaj-Saleh A Odat; Moayyad Shawaqfah; Fares Al-Momani; Bashar Shboul

doi:10.1016/j.dib.2023.109853

Data of simulation model for photovoltaic system's maximum power point tracking using sequential Monte Carlo algorithm

Data Brief. 2023 Nov 26:52:109853. doi: 10.1016/j.dib.2023.109853. eCollection 2024 Feb.

Authors

Alhaj-Saleh A Odat¹, Moayyad Shawaqfah², Fares Al-Momani³, Bashar Shboul¹

Affiliations

¹ Department of Renewable Energy Engineering, Al Al-Bayt University, Mafraq, Jordan.
² Department of Civil Engineering, Al Al-Bayt University, Mafraq, Jordan.
³ Department of Chemical Engineering, college of Engineering, Qatar University, Doha, Qatar.

Abstract

This article outlines the input data and partial shading conditions employed in the replication model of Sequential Monte Carlo (SMC)-based tracking techniques for photovoltaic (PV) systems. The model aims to compare the performance of classical perturb and observe (P&O) algorithm, particle swarm optimization (PSO) algorithm, flower pollination algorithm (FPA), and SMC-based tracking techniques. The mathematical design and methodology of the complete PV system were detailed in our prior research, titled "Dynamic and Adaptive Maximum Power Point Tracking Using Sequential Monte Carlo Algorithm for Photovoltaic System" by Odat et al. (2023) [1]. The provided data facilitate precise replication of the output, saving significant simulation time. Additionally, these data can be readily applied to compare algorithmic results referenced by (Babu, T.S. et al., 2015; PrasanthRam, J. et al., 2017) [2,3], and contribute to the development of new processes for practical applications.

Keywords: Comparison of maximum power point tracking techniques; Dynamic partial shading weather conditions; PV simulink replication model; Random irradiance and temperature waveforms for PV systems; Simulation of sequential Monte Carlo.