Parameter Identification of Solar Cell Mathematical Models Using Metaheuristic Algorithms

A highly accurate modeling of photovoltaic (PV) systems from experimental data is a very important task for electronic engineers for efficient design of PV systems. Suitable optimization techniques are required for this kind of computational modeling. In this work, a Modified Elephant Swarm Water Search Algorithm (MESWSA) has been proposed for efficient and accurate estimation of the parameters of solar cells and PV modules. Initially, the algorithm was tested on two different benchmark PV systems, i.e. a solar cell and PV module using the single-diode model and the double-diode model. The experimental results and comparisons with some other optimization technique clearly reveal the effectiveness of this proposed algorithm in terms of the accuracy of final solutions, success rate, convergence speed, and stability. Next, the proposed MESWSA optimization technique has also been tested against the manufacturer's datasheet of two industrial solar modules. Results show the efficiency of MESWSA algorithm for I-V characteristics of solar modules at different operating conditions. Therefore, this MESWSA can serve as a new alternative metaheuristic for parameter estimation of solar cells/PV modules. Abbreviations Modified Elephant Swarm Water Search Algorithm (MESWSA) Photovoltaic (PV)

Among all renewable energy sources, solar cells are considered the most popular solution for a clean source of energy and have a wide range of applications from few watts to Megawatt industrial and domestic loads. Building a precise mathematical model based on nonlinear equations for solar cells as well as photovoltaic (PV) modules is an essential issue for reasonable performance assessment, control and optimal operation of PV energy systems. In the current study, a novel optimization algorithm, Tree Growth Algorithm (TGA), is applied for accurate and efficient extraction of the unknown solar cell and PV module parameters. TGA is applied for estimating the unidentified parameters of PV models. Single diode model (SDM), double diode model (DDM) and three diode model (TDM) are investigated in the mathematical models of both solar cells and PV modules. The obtained results from the application of TGA to achieve this objective are compared with different algorithms reported in the literature. Moreover, the results demonstrated that the proposed algorithm of TGA superior to other reported methods. The good matching of the I-V characteristic curve of the computed parameters with those of the measured data from the manufacturer’s PV modules/cells datasheet proved that the proposed TGA may function as a competitor to the methods provided in literature for parameters’ identification of PV of solar cells.

Solar photovoltaic (PV) panels are projected to become the largest contributor of clean electricity generation worldwide. Maintenance and cleaning strategies are crucial for optimizing solar PV operations, ensuring a satisfactory economic return of investment. Nanocoating may have potential for optimizing PV operations; however, there is insufficient scientific evidence that supports this idea. Therefore, this study aims to investigate the effectiveness of nanocoating on the performance of solar photovoltaic (PV) panels installed in Al Seeb, Oman. A further study was also carried out to observe the influence of coating layers on the performance of PV panels. One SiO2 nanocoated solar panel, another regularly cleaned PV panel, and a reference uncleaned panel were used to carry out the study. The site of the study was treeless and sandy, with a hot and dry climate. A data logger was connected to the solar PV panel and glass panel to record the resulting voltage, current, temperature, and solar radiation. It was observed that nanocoated PV panels outperformed both regular PV panels and uncleaned PV panels. Nanocoated PV panels demonstrated an average efficiency of 21.6%, showing a 31.7% improvement over uncleaned panels and a 9.6% improvement over regularly cleaned panels. Although nanocoating displayed high efficiency, regular cleaning also contributes positively. Furthermore, even though nanocoated PV panels outperformed the other two panels, it is important to note that the performance difference between the regular cleaned PV panels and the nanocoated PV panels was small. This indicates that regular cleaning strategies and nanocoating can further contribute to maintaining a more efficient solar PV system. Coating in many layers was also observed to influence the performance of PV panels insignificantly, mainly the fourth layer coating appeared to have formed sufficient mass to retain heat.

This paper presents a critical analysis of the meta-heuristic techniques used in various researches on the optimisation of photovoltaic (PV) parameters, which involves the use of different algorithms in order to extract and improve these parameters from the single diode model (SDM), double diode model (DDM) and three diode model (TDM) respectively. The modelling parameters such as the photon current, saturation current, the series and parallel resistances are investigated to understand the optimum value. It will also equate the results of datasheet values from PV manufactures with experiment values obtained from PV module measurements. The meta-heuristics techniques to be considered include genetic algorithm (GA), particle swarm optimisation (PSO), harmony search (HS), flower pollination algorithm (FPA), simulated annealing (SA), teaching learning based optimisation (TLBO), and other different hybrid solutions to optimize the convergence speed. Root mean square error (RMSE) is used as a performance indicator of each meta-heuristic technique. These optimisation techniques are utilised in extracting the parameters of a 5 W polycrystalline panel at standard testing conditions. The results presented in this paper compared the performances of the mentioned meta-heuristics on the single, double and triple diode models respectively.

A photovoltaic (PV) module is an equipment that converts solar energy to electrical energy. A mathematical model should be presented to show the behavior of this device. The well-known single-diode and double-diode models are utilized to demonstrate the electrical behavior of the PV module. “Matlab/Simulink” is used to model and simulate the PV models because it is considered a major software for modeling, analyzing, and solving dynamic system real problems. In this work, a new modeling method based on the “Multiplexer and Functions blocks” in the "Matlab/Simulink Library" is presented. The mathematical analysis of single and double diodes is conducted on the basis of their equivalent circuits with simple modification. The corresponding equations are built in Matlab by using the proposed method. The unknown internal parameters of the PV panel circuit are extracted by using the PV array tool in Simulink, which is a simple method to obtain the PV parameters at certain weather conditions. Double-diode model results are compared with the single-diode model under various irradiances and temperatures to verify the performance and accuracy of the proposed method. The proposed method shows good agreement in terms of the I–V and P–V characteristics. A monocrystalline NST-120 W PV module is used to validate the proposed method. This module is connected to a variable load and tested for one summer day. The experimental voltage, current, and power are obtained under various irradiances and temperatures, and the I–V and P–V characteristics are obtained.

It is important to investigate the long-term performances of an accurate modeling of photovoltaic (PV) systems, especially in the prediction of output power, with single and double diode models as the configurations mainly applied for this purpose. However, the use of one configuration to model PV panel limits the accuracy of its predicted performances. This paper proposes a new hybrid approach based on classification algorithms in the machine learning framework that combines both single and double models in accordance with the climatic condition in order to predict the output PV power with higher accuracy. Classification trees, k-nearest neighbor, discriminant analysis, Naïve Bayes, support vector machines (SVMs), and classification ensembles algorithms are investigated to estimate the PV power under different conditions of the Mediterranean climate. The examined classification algorithms demonstrate that the double diode model seems more relevant for low and medium levels of solar irradiance and temperature. Accuracy between 86% and 87.5% demonstrates the high potential of the classification techniques in the PV power predicting. The normalized mean absolute error up to 1.5% ensures errors less than those obtained from both single-diode and double-diode equivalent-circuit models with a reduction up to 0.15%. The proposed hybrid approach using machine learning (ML) algorithms could be a key solution for photovoltaic and industrial software to predict more accurate performances.

This paper compares two methods for extraction of photovoltaic (PV) module parameters. The numerical method called Gauss-Seidel, used on the single diode model, and the analytical method applied on the double diode model. Both methods are based on the manufacturer’s datasheets to extract the five parameters. These methods are validated using monocrystalline PV module. The modified mathematical modeling equations are classified into models 1 to 5. Only model 2 and model 4 are used for the analytical method. Each model is based on the different combinations of the ideality factor, the series resistance, photocurrent and saturation current plotted under varying temperature and irradiance. The accuracy of PV module based on both methods is evaluated by Absolute Error (AE). In order to evaluate the effectiveness and accuracy of the proposed methods, the equations were implemented in the MATLAB/Simulink environment and the results obtained are compared with extracted experimental data.

Photovoltaic parameter estimation using honey badger algorithm and African vulture optimization algorithm

The thermal coefficient of a solar photovoltaic (PV) panel is a value that is provided with its specification sheet and tells us precisely the drop in panel performance with rising temperature. In desert climates, the PV panel temperatures are known to reach above 70 degrees centigrade. Exploring effective methods of increasing energy transfer efficiency is the issue that attracts researchers nowadays, which also contributes to reducing the cost of using solar photovoltaic (PV) systems with storage batteries. Temperature handling of solar PV modules is one of the techniques that improve the performance of such systems by cooling the bottom surface of the PV panels. This study initially reviews the effective methods of cooling the solar modules to select a proper, cost-effective, and easy to implement one. An active fan-based cooling method is considered in this research to make ventilation underneath the solar module. A portion of the output power at a prespecified high level of battery state-of-charge (SOC) is used to feed the fans. The developed comparator circuit is used to control the power ON/OFF of the fans. A Matlab-based simulation is employed to demonstrate the power rate improvements and that consumed by the fans. The results of simulations show that the presented approach can achieve significant improvements in the efficiency of PV systems that have storage batteries. The proposed method is demonstrated and evaluated for a 1.62 kW PV system. It is found from a simultaneous practical experiment on two identical PV panels of 180 W over a full day that the energy with the cooling system was 823.4 Wh, while that without cooling was 676 Wh. The adopted approach can play a role in enhancing energy sustainability.

In this paper the modelling of the photovoltaic module has been applied through the experimental flash test data of industrial photovoltaic module. The theoretical background of the single diode model and the double diode model of the photovoltaic module have been described and similar procedures for the extraction and estimation of the unknown parameters have been proposed. The Levenberg-Marquardt optimization algorithm has been used to minimize objective function as a set of five nonlinear equations that were written for five equilibrium points on the current-voltage curve of the photovoltaic module. The single diode model and double diode model have then been simulated with the estimated unknown parameters and a very good matching between flash test data and a simulation data.

The estimation of the power values obtained from photovoltaic (PV) systems is of critical importance for the reliable and economical use of solar energy panels. This estimation affects many processes, starting from the installation phase of solar panels to guiding electricity companies, energy management, and distribution. At the same time, it is necessary to detect the adaptations of solar panels in a timely manner and reach the optimal production capacity to provide the most efficient energy production. In this context, Artificial Neural Networks (ANN) were used to estimate the power values obtained from PV panels. In this study, heuristic algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Clonal Selection Algorithm (CSA), Ant Colony Optimization, and Artificial Bee Colony (ABC) were used to estimate the power values obtained from monocrystalline and polycrystalline photovoltaic panels. In the verification of the estimation results, the most common statistical evaluation criteria, Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and Variance (R2) equations were used. The estimation values made with the PSO algorithm were the closest to the real values. 98.95% estimation was achieved in monocrystalline photovoltaic solar panels and 93.94% in polycrystalline photovoltaic solar panels.

This study intends to better solar photovoltaic (PV) panel performance by employing anti-reflective coating and explore how dust affects solar panel effectiveness. Three equivalent solar PV panels were compared, having one of them being uncoated, the next one having a TiO2 nanomaterial coating, and the very last one having a SiO2 nanomaterial coating. PV panel surfaces are coated with superhydrophilicity TiO2 as well as superhydrophobic SiO2 nanomaterials using a cloth made of microfibers. With the aid of a photovoltaic (PV) analyser, the power output of each and every PV panel has been monitored during the month of November 2021. After one month of being exposed to the environment, the percentage improvement in efficiency for TiO2-coated panels was 7.66% and for SiO2 coated panels was 19.73% as compared to uncoated PV panels. Results demonstrate that SiO2 covered PV panels outperform the other two scenarios in terms of efficiency and power output. The frequency of photovoltaic panel washing is reduced by the application of coating. Different amounts of dust are evenly scattered on the surface of the PV panel in order to observe the effect of the dust. Additionally, as the amount of dust increases, the effectiveness of PV panels declines considerably. When 20g of dust is dispersed across the surface of a PV panel, its efficiency falls by 34.6 percent.

Solar photovoltaic (PV) modules are made up of with 32, 36, 48, 60, 72, and 90 number of series connected solar cells, depending on the size and maximum power output of the module. When one solar cell or group of solar cells in PV module (PVM) are partially or fully shaded, the output power gets reduced due to power mismatches, and which can also cause a hotspot heating problem in a PV modules. This research paper focuses on the development of new PV modules (based on arrangement of solar cells within a PVM) with cell-level configurations (PVM-CLC) such as series connection module (SCM), series–parallel connection module (SPCM), total cross-tied connection module (TCTCM), bridge-linked connection module (BLCM), and honey-comb connection module (HCCM) with number of bypass diodes (BDs) to minimize the partial shading effect on PV modules. For this investigation, 36 solar cells were used to develop the new 40 W PV modules. And MATLAB/Simulink software was used to analyze the performance of developed PVM in terms of global maximum peak power and mismatch losses under different cell-level shading cases/conditions. The maximum power results are obtained under considered cell-level shading cases shows that the TCTCM outperforms other cell-level configuration modules. The main objective of this study is to get more power output under shading conditions by developing new PV modules. The proposed PVM-CLC not only reduces the mismatch power loss but also makes installation easier and less complicated when compared to array configurations.KeywordsSolar PV moduleCell-level configurationsCell-level shading casesBypass diodeGlobal maximum peak powerMismatch loss

Although Egypt falls within the solar belt, solar energy use on the household level remains very sparse. This research examines the factors driving and hindering households’ willingness to shift from fossil fuels to solar energy. The study evaluates the socioeconomic determinants of households’ willingness to adopt photovoltaic (PV) solar panels, based on a random distribution of 301 questionnaires in Cairo and Giza – two of Egypt’s most densely populated governorates. Two models were used? a Logistic Model and a Structural Equations Model (SEM) that regresses the willingness to install PV panels on household characteristics. For a deeper analysis of how socioeconomic differences shape renewable energy adoption, we separated the SEM model into behavioural and economic characteristics to be able to identify whether willingness to adopt solar PV systems, (or the lack of), stem from either set of factors. Accordingly, these insights can inform policy and support the implementation of targeted actions in both directions. Behavioural factors include household heads’ willingness to take a credit facility to finance solar PV panels, household heads’ level of education, living in rural versus urban areas, household size, whether any of the household members needed medical attention owing to heat waves during past year, household heads’ age and gender. Economic variables include household heads’ monthly income, whether the household owns a farm, whether the household owns gas at home, access to internet connection at home, access to credit facility, the number of times on average the household was disconnected from electricity per week during the past year, and education reflecting the socioeconomic level of the household head. Behavioural variables that positively contribute to adopting solar panels are revealed to be willingness to take a credit facility, age, education and household members needing medical attention owing to heat waves during past year; behavioural variables that contribute negatively to solar panel adoption include gender of the household head (in favour of females) and the geographical dimension (in favour of rural areas). The economic model’s results reveal a positive relationship for the average monthly income of the household head, ownership of farmland, and the years of education of the household head. Households with access to internet at home is negatively associated with the willingness to install solar panels. Suggested policy implications include packages that target programs supporting low-income households. Keywords: PV Solar Panels, Behavioural & Socioeconomic determinants, Structural Equation Model, Logistic Regression Model

Solar radiation is increasingly used as a clean energy source, and photovoltaic (PV) panels that contain solar cells (SCs) transform solar energy into electricity. The current-voltage characteristics for PV models is nonlinear. Due to a lack of data on the manufacturer's datasheet for PV models, there are several unknown parameters. It is necessary to accurately design the PV systems by defining the intrinsic parameters of the SCs. Various methods have been proposed to estimate the unknown parameters of PV cells. However, their results are often inaccurate. In this article, a gradient-based optimizer (GBO) was applied as an efficient and accurate methodology to estimate the parameters of SCs and PV modules. Three common SC models, namely, single-diode models (SDMs), double-diode models (DDMs), and three-diode models (TDMs) were used to demonstrate the capacity of the GBO to estimate the parameters of SCs. The proposed GBO algorithm for estimating the optimal values of the parameters for various SCs models are applied on the real data of a 55 mm diameter commercial R.T.C-France SC. Comparison between the GBO and other algorithms are performed for the same data set. The smallest value of the error between the experimental and the simulated data is achieved by the proposed GBO. Also, high closeness between the simulated P-V and I-V curves is achieved by the proposed GBO compared with the experimental.