scholarly journals Parameter Identification of Photovoltaic Cell Model Using Modified Elephant Herding Optimization-Based Algorithms

2021 ◽  
Vol 11 (24) ◽  
pp. 11929
Author(s):  
Amer Malki ◽  
Abdallah A. Mohamed ◽  
Yasser I. Rashwan ◽  
Ragab A. El-Sehiemy ◽  
Mostafa A. Elhosseini
Keyword(s):  
Solar Cell ◽  
Cell Model ◽  
Optimization Algorithms ◽  
Metaheuristic Algorithms ◽  
Local Optimum ◽  
Cell Models ◽  
Unknown Parameters ◽  
Solar Module ◽  
Cell Parameters ◽  
Pv Cell

The use of metaheuristics in estimating the exact parameters of solar cell systems contributes greatly to performance improvement. The nonlinear electrical model of the solar cell has some parameters whose values are necessary to design photovoltaic (PV) systems accurately. The metaheuristic algorithms used to determine solar cell parameters have achieved remarkable success; however, most of these algorithms still produce local optimum solutions. In any case, changing to more suitable candidates through elephant herd optimization (EHO) equations is not guaranteed; in addition, instead of making parameter α adaptive throughout the evolution of the EHO, making them adaptive during the evolution of the EHO might be a preferable choice. The EHO technique is used in this work to estimate the optimum values of unknown parameters in single-, double-, and three-diode solar cell models. Models for five, seven, and ten unknown PV cell parameters are presented in these PV cell models. Applications are employed on two types of PV solar cells: the 57 mm diameter RTC Company of France commercial silicon for single- and double-diode models and multi-crystalline PV solar module CS6P-240P for the three-diode model. The total deviations between the actual and estimated result are used in this study as the objective function. The performance measures used in comparisons are the RMSE and relative error. The performance of EHO and the proposed three improved EHO algorithms are evaluated against the well-known optimization algorithms presented in the literature. The experimental results of EHO and the three improved EHO algorithms go as planned and proved to be comparable to recent metaheuristic algorithms. The three EHO-based variants outperform all competitors for the single-diode model, and in particular, the culture-based EHO (CEHO) outperforms others in the double/three-diode model. According the studied cases, the EHO variants have low levels of relative errors and therefore high accuracy compared with other optimization algorithms in the literature.

scholarly journals Identification of Parameters in Photovoltaic Models through a Runge Kutta Optimizer

Mathematics ◽  
2021 ◽  
Vol 9 (18) ◽  
pp. 2313
Author(s):  
Hassan Shaban ◽  
Essam H. Houssein ◽  
Marco Pérez-Cisneros ◽  
Diego Oliva ◽  
Amir Y. Hassan ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Solar Cell ◽  
Renewable Energy Sources ◽  
Cell Models ◽  
Unknown Parameters ◽  
Cell Parameters ◽  
Hunger Games ◽  
Sine Cosine Algorithm ◽  
Swarm Algorithm ◽  
Runge Kutta

Recently, the resources of renewable energy have been in intensive use due to their environmental and technical merits. The identification of unknown parameters in photovoltaic (PV) models is one of the main issues in simulation and modeling of renewable energy sources. Due to the random behavior of weather, the change in output current from a PV model is nonlinear. In this regard, a new optimization algorithm called Runge–Kutta optimizer (RUN) is applied for estimating the parameters of three PV models. The RUN algorithm is applied for the R.T.C France solar cell, as a case study. Moreover, the root mean square error (RMSE) between the calculated and measured current is used as the objective function for identifying solar cell parameters. The proposed RUN algorithm is superior compared with the Hunger Games Search (HGS) algorithm, the Chameleon Swarm Algorithm (CSA), the Tunicate Swarm Algorithm (TSA), Harris Hawk’s Optimization (HHO), the Sine–Cosine Algorithm (SCA) and the Grey Wolf Optimization (GWO) algorithm. Three solar cell models—single diode, double diode and triple diode solar cell models (SDSCM, DDSCM and TDSCM)—are applied to check the performance of the RUN algorithm to extract the parameters. the best RMSE from the RUN algorithm is 0.00098624, 0.00098717 and 0.000989133 for SDSCM, DDSCM and TDSCM, respectively.

scholarly journals A Population Classification Evolution Algorithm for the Parameter Extraction of Solar Cell Models

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Yiqun Zhang ◽  
Peijie Lin ◽  
Zhicong Chen ◽  
Shuying Cheng
Keyword(s):  
Solar Cell ◽  
Good Accuracy ◽  
Parameter Extraction ◽  
Local Optimum ◽  
Cell Models ◽  
Small Probability ◽  
Population Classification ◽  
Evolution Algorithm ◽  
Bird Mating Optimizer ◽  
Different Sources

To quickly and precisely extract the parameters for solar cell models, inspired by simplified bird mating optimizer (SBMO), a new optimization technology referred to as population classification evolution (PCE) is proposed. PCE divides the population into two groups, elite and ordinary, to reach a better compromise between exploitation and exploration. For the evolution of elite individuals, we adopt the idea of parthenogenesis in nature to afford a fast exploitation. For the evolution of ordinary individuals, we adopt an effective differential evolution strategy and a random movement of small probability is added to strengthen the ability to jump out of a local optimum, which affords a fast exploration. The proposed PCE is first estimated on 13 classic benchmark functions. The experimental results demonstrate that PCE yields the best results on 11 functions by comparing it with six evolutional algorithms. Then, PCE is applied to extract the parameters for solar cell models, that is, the single diode and the double diode. The experimental analyses demonstrate that the proposed PCE is superior when comparing it with other optimization algorithms for parameter identification. Moreover, PCE is tested using three different sources of data with good accuracy.

Identification of unknown parameters of solar cell models: A comprehensive overview of available approaches

2018 ◽  
Vol 90 ◽  
pp. 453-474 ◽  
Author(s):  
Rabeh Abbassi ◽  
Abdelkader Abbassi ◽  
Mohamed Jemli ◽  
Souad Chebbi
Keyword(s):  
Solar Cell ◽  
Cell Models ◽  
Unknown Parameters ◽  
Comprehensive Overview

scholarly journals A Powerful Bio-Inspired Optimization Algorithm Based PV Cells Diode Models Parameter Estimation

2021 ◽  
Vol 9 ◽  
Author(s):  
Liming Sun ◽  
Jingbo Wang ◽  
Lan Tang
Keyword(s):  
Heuristic Algorithms ◽  
Simulation Analysis ◽  
Dynamic Balance ◽  
Maximum Output Power ◽  
Optimization Methods ◽  
Maximum Output ◽  
Algorithm Optimization ◽  
Unknown Parameters ◽  
Cell Parameters ◽  
Pv Cell

Accurate and reliable photovoltaic (PV) cell parameter identification is critical to simulation analysis, maximum output power harvest, and optimal control of PV systems. However, inherent high-nonlinear and multi-modal characteristics usually result in thorny obstacles to hinder conventional optimization methods to obtain a fast and satisfactory performance. In this study, a novel bio-inspired grouped beetle antennae search (GBAS) algorithm is devised to effectively identify unknown parameters of three different PV models, i.e., single diode model (SDM), double diode model (DDM), and triple diode model (TDM). Compared against beetle antennae search (BAS) algorithm, optimization efficiency of GBAS algorithm is markedly enhanced based on a cooperative searching group that consists of multiple individuals rather than a single beetle. Besides, a dynamic balance mechanism between local exploitation and global exploration is designed to increase the probability for a higher quality optimum. Comprehensive case studies demonstrate that GBAS algorithm can outperform other advanced meta-heuristic algorithms in both optimization precision and stability for estimating PV cell parameters, e.g., standard deviation (SD) of root mean square error (RMSE) obtained by GBAS algorithm is 64.34% smaller than the best value obtained by other algorithms in SDM, 61.86% smaller than that in DDM.

scholarly journals Parameter Optimization of Single-Diode Model of Photovoltaic Cell Using Memetic Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Yourim Yoon ◽  
Zong Woo Geem
Keyword(s):  
Solar Cell ◽  
Least Squares Method ◽  
Memetic Algorithm ◽  
Cell Model ◽  
Harmony Search ◽  
Cuckoo Search ◽  
Photovoltaic Cell ◽  
Pattern Search ◽  
Cell Parameters ◽  
Local Searches

This study proposes a memetic approach for optimally determining the parameter values of single-diode-equivalent solar cell model. The memetic algorithm, which combines metaheuristic and gradient-based techniques, has the merit of good performance in both global and local searches. First, 10 single algorithms were considered including genetic algorithm, simulated annealing, particle swarm optimization, harmony search, differential evolution, cuckoo search, least squares method, and pattern search; then their final solutions were used as initial vectors for generalized reduced gradient technique. From this memetic approach, we could further improve the accuracy of the estimated solar cell parameters when compared with single algorithm approaches.

scholarly journals A Novel High Accuracy PV Cell Model Including Selfheating and Parameter Variation

2017 ◽  
Author(s):  
Aurel Gontean ◽  
Septimiu Lica ◽  
Szilard Bularka ◽  
Roland Szabo ◽  
Dan Lascu
Keyword(s):  
Cell Model ◽  
High Accuracy ◽  
Parameter Variation ◽  
Internal Temperature ◽  
Cell Parameters ◽  
Self Heating ◽  
Parasitic Resistance ◽  
Pv Cell ◽  
Parameter Variance ◽  
Novel Model

This paper proposes a novel model for a PV cell with parameters variance dependency on temperature and irradiance included. The model relies on commercial available data, calculates the cell parameters for standard conditions and then extrapolates them for the whole operating range. An up-to-date review of the PV modeling is also included with series and parallel parasitic resistance values and dependencies discussed. The parameters variance is analyzed and included in the proposed PV model, where the self-heating phenomenon is also considered. Each parameter variance is compared to the results from different authors. The model includes only standard components and can be run on any SPICE-based simulator. Unlike other approaches that consider the internal temperature as a parameter, our proposal relies on air temperature as an input and computes the actual internal temperature accordingly. Finally, the model is validated via experiments and comparisons to similar approaches are provided.

scholarly journals Application of Symbiotic Organisms Search Algorithm for Parameter Extraction of Solar Cell Models

2018 ◽  
Vol 8 (11) ◽  
pp. 2155 ◽  
Author(s):  
Guojiang Xiong ◽  
Jing Zhang ◽  
Xufeng Yuan ◽  
Dongyuan Shi ◽  
Yu He
Keyword(s):  
Solar Cell ◽  
Search Algorithm ◽  
Parameter Extraction ◽  
Cell Models ◽  
Unknown Parameters ◽  
Pv System ◽  
Symbiotic Interaction ◽  
Solution Quality ◽  
Symbiotic Organisms Search ◽  
Symbiotic Organisms

Extracting accurate values for relevant unknown parameters of solar cell models is vital and necessary for performance analysis of a photovoltaic (PV) system. This paper presents an effective application of a young, yet efficient metaheuristic, named the symbiotic organisms search (SOS) algorithm, for the parameter extraction of solar cell models. SOS, inspired by the symbiotic interaction ways employed by organisms to improve their overall competitiveness in the ecosystem, possesses some noticeable merits such as being free from tuning algorithm-specific parameters, good equilibrium between exploration and exploitation, and being easy to implement. Three test cases including the single diode model, double diode model, and PV module model are served to validate the effectiveness of SOS. On one hand, the performance of SOS is evaluated by five state-of-the-art algorithms. On the other hand, it is also compared with some well-designed parameter extraction methods. Experimental results in terms of the final solution quality, convergence rate, robustness, and statistics fully indicate that SOS is very effective and competitive.

scholarly journals Simple vs complex models for solar cells

2019 ◽  
Vol 85 ◽  
pp. 04004
Author(s):  
Andreea Săbăduş ◽  
Marius Paulescu
Keyword(s):  
Solar Cell ◽  
Cell Model ◽  
Specific Model ◽  
Photovoltaic Module ◽  
Model Parameters ◽  
Cell Parameters ◽  
Large Dispersion ◽  
Current Voltage ◽  
Complex Models ◽  
Current Voltage Characteristics

In this paper three different procedures for extracting the current-voltage characteristics of a crystalline photovoltaic module are studied. Each procedure is associated to a solar cell model characterized by a well-defined degree of complexity. The results emphasize that the simple models approximate the current-voltage characteristics of a solar cell as good as the more complex models. Even if all the procedures analysed in this paper approximate well the measured characteristics, the specific model parameters experience a large dispersion. From a broader perspective, the results raise a question mark on the ability of the current procedures to accurately extract the solar cell parameters.

scholarly journals Modeling and Simulation of Photovoltaic Cell using Single Diode Solar Cell and Double Diode Solar Cell Model

2019 ◽  
Vol 8 (10) ◽  
pp. 558-565 ◽  
Keyword(s):  
Solar Cells ◽  
Solar Cell ◽  
Modeling And Simulation ◽  
Cell Model ◽  
Photovoltaic Cells ◽  
Photovoltaic Cell ◽  
Photovoltaic Module ◽  
Pv Systems ◽  
Pv Cell ◽  
Factor Α

Modeling and simulation of photovoltaic cells or PV cell is becoming important as it provides an easy platform to perform studies on photovoltaic cells and the design and analysis of the system based on photovoltaic cells. In this paper, we present our study of the ordinary photovoltaic module on the basis of one diode and two diode models. Studies are extended to solar cells as solar cells are similar to photodiodes. Performance of the solar cells may be described in terms of ideality factor (α), which decreases with temperature and is observed to affect the performance of the PV cell. PV systems exhibit better performance with diodes having higher values for α. In this paper, our efforts are to study the effects of α on Current and Power versus Voltage characteristics of the solar cells. MATLAB simulation of solar cell systems is a simple and elegant mechanism useful for designing and modeling the framework of the solar power plant

scholarly journals THREE-DIODE MODEL AND SIMULATION OF PHOTOVOLTAIC (PV) CELLS

2019 ◽  
pp. 108-116
Keyword(s):  
Cell Model ◽  
Short Circuit ◽  
Short Circuit Current ◽  
Photovoltaic Cell ◽  
Open Circuit ◽  
Cell Parameters ◽  
Model And Simulation ◽  
Pv Cell ◽  
Mathematical Equations ◽  
Model Equations

Existing empirical solar cell models use one or two diodes. As the number of diodes in a model increases, the mathematical complexity in deriving model equations also increases. In this paper, a photovoltaic cell is modeled using three diodes. Non-linear mathematical equations governing the I-V and P-V characteristics are summarized and simulated using Matlab looping iterative method. All simulations were performed in Matlab. Comparison is made between all models (one, two and three-diode) for design verification. Results obtained show that as the number of diodes increases in a PV cell model, the open circuit voltage and maximum power decreases for a given set of PV cell parameters. The short circuit current remained at a fixed value irrespective of the number of diodes.

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