Salp Swarm Optimization Algorithm for Estimating the Parameters of Photovoltaic Panels Based on the Three-Diode Model

Jhon Montano,Andres Felipe Tobon Mejia,Andrés Alfonso Rosales Muñoz,José Mena Palomeque,Fabio Andrade,Oscar D Garzon Rivera

doi:10.3390/electronics10243123

Jhon Montano, Andres Felipe Tobon Mejia + Show 4 more

Open Access

https://doi.org/10.3390/electronics10243123

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Abstract

Due to the the lack of information about parameters in the datasheets of photovoltaic (PV) panels, it is difficult to study their modeling because PV behavior is based on voltage–current (V-I) data, which present a highly nonlinear relationship. To solve this difficulty, this study presents a mathematical three-diode model of a PV panel that includes multiple unknown parameters: photoinduced current, saturation currents of the three diodes, three ideality factors, serial resistance, and parallel resistance. These parameters should be estimated in the three-diode model of a PV panel to obtain the actual values that represent the voltage–current profile or the voltage–power profile (because of its visual simplicity) of the PV panel under analysis. In order to solve this problem, this paper proposes a new application of the salp swarm algorithm (SSA) to estimate the parameters of a three-diode model of a PV panel. Two test scenarios were implemented with two different PV panels, i.e., Kyocera KC200GT and Solarex MSX60, which generate different power levels and are widely used for commercial purposes. The results of the simulations were obtained using different irradiance levels. The proposed PV model was evaluated based on the experimental results of the PV modules analyzed in this paper. The efficiency of the optimization technique proposed here, i.e., SSA, was measured by a fair comparison between its numerical results and those of other optimization techniques tuned to obtain the best response in terms of the objective function.

Highlights

The global energy demand has reached 162.194 TWh according to studies conducted by Oxford in 2019 [1], and 79.7% of this energy comes from fossil fuels [2]
Certain constant values are necessary for the three-diode model (TDM) to work properly for the parameter estimation process, such as the Boltzmann constant k = 1.3806503 × 10−23 (J/K), temperature in degrees Kelvin 273 (◦K), and electrical charge q = 1.60217646 × 10−19, which can be applied to any type of PV panel
To estimate the parameters of the TDM and validate the effectiveness of the methodology proposed here (SSA) in terms of the solution quality, this study implemented three additional continuous solution methods: (1) particle swarm optimization (PSO) [56], which is a bioinspired algorithm based on the behavior of schools of fish, flocks of birds, and other animals that move in big groups; (2) the continuous genetic algorithm (CGA) [57], which is based on the genetic process of living beings that store information about learned evolution and transfer said information to the generations; (3) the sine cosine algorithm (SCA) [58], which is a powerful metaheuristic optimization technique and a variant of the conventional particle swarm optimization approaches

Summary

Introduction

The global energy demand has reached 162.194 TWh according to studies conducted by Oxford in 2019 [1], and 79.7% of this energy comes from fossil fuels [2]. There is an excessive concentration of airborne particulate matter of a size lower than 2.5 μm (in g of particles/m3 of air, i.e., PM2.5) in the atmosphere, which leads to higher human mortality and global disease rates [3,4,5]. To fight such excessive consumption of fossilderived fuels, new alternatives have emerged to supply power, such as wind, hydro, tidal, geothermal, nuclear, and solar energy. They use infinite energy that does not damage the environment

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