Parameter Extraction of Solar Cell Models Using the Lightning Search Algorithm in Different Weather Conditions

2016 ◽  
Vol 138 (4) ◽  
Author(s):  
Reza Sirjani ◽  
Hussain Shareef
Keyword(s):  
Experimental Data ◽  
Solar Cells ◽  
Entire Range ◽  
Search Algorithm ◽  
Weather Conditions ◽  
Optimization Method ◽  
Parameter Extraction ◽  
Solar Panels ◽  
Bee Colony ◽  
Pv Module

Recently, accurate modeling of the differences between the current and voltage (I–V) characteristics of solar cells has been the main focus of many research studies. Mostly the results were obtained only for single diode or double diode solar cells, not for both or even for photovoltaic (PV) modules. Moreover, the effect of different shading conditions and different temperatures should be considered; otherwise, the obtained results would be reliable for specific weather conditions and unreliable for all real conditions. In this study, a novel nature-inspired optimization method known as the lightning search algorithm (LSA) was developed to extract the parameters of single diode and double diode solar cells as well as for a PV module. LSA is formulated based on lightning, which originates from thunderstorms. Experimental data from multicrystalline KC200GT solar panels were used to test the single diode and double diode solar panel models, and experimental data from the monocrystalline SQ150-PC solar panels were used to test the PV module model. The experimental data are first collected at the same temperature at five different irradiance levels. In the second stage, variations in temperature are considered at the same irradiance level. The extraction results in the LSA I–V curves accurately fit the entire range of the experimental data, while many fluctuations were seen in the particle swarm optimization (PSO) and bee colony optimization (BCO) I–V curves. The convergence characteristics of LSA were also evaluated in terms of accuracy and speed. For all cases, when LSA was used, the accuracies matched well with the entire range of experimental data. In addition, the value of the objective function using LSA was lower, and that method converged much faster than PSO and BCO.

scholarly journals Delamination-and Electromigration-Related Failures in Solar Panels—A Review

2021 ◽  
Vol 13 (12) ◽  
pp. 6882
Author(s):  
Abdulwahab A. Q. Hasan ◽  
Ammar Ahmed Alkahtani ◽  
Seyed Ahmad Shahahmadi ◽  
Mohammad Nur E. Alam ◽  
Mohammad Aminul Islam ◽  
...  
Keyword(s):  
Failure Modes ◽  
Weather Conditions ◽  
Solar Panels ◽  
Power Losses ◽  
Pv Module ◽  
Humidity Change ◽  
Aging Test ◽  
Pv Modules ◽  
Harsh Climate ◽  
Silicon Based

The reliability of photovoltaic (PV) modules operating under various weather conditions attracts the manufacturer’s concern since several studies reveal a degradation rate higher than 0.8% per year for the silicon-based technology and reached up to 2.76% per year in a harsh climate. The lifetime of the PV modules is decreased because of numerous degradation modes. Electromigration and delamination are two failure modes that play a significant role in PV modules’ output power losses. The correlations of these two phenomena are not sufficiently explained and understood like other failures such as corrosion and potential-induced degradation. Therefore, in this review, we attempt to elaborate on the correlation and the influence of delamination and electromigration on PV module components such as metallization and organic materials to ensure the reliability of the PV modules. Moreover, the effects, causes, and the sites that tend to face these failures, particularly the silicon solar cells, are explained in detail. Elsewhere, the factors of aging vary as the temperature and humidity change from one country to another. Hence, accelerated tests and the standards used to perform the aging test for PV modules have been covered in this review.

Accurate Method for Loss Parameter Extraction of Solar Panels

2018 ◽  
Vol 140 (2) ◽  
Author(s):  
M. Bencherif ◽  
B. N. Brahmi
Keyword(s):  
Experimental Data ◽  
Computational Procedure ◽  
Accurate Method ◽  
Low Complexity ◽  
Parameter Extraction ◽  
Solar Panels ◽  
Fixed Temperature ◽  
Irradiance Level ◽  
Power Point ◽  
Two Parameters

This work describes a new simple and effective method to extract the loss parameters of solar panels (solar cells) and able to accurately represent their electrical behavior. This approach allows the extraction of the parameters of the single diode model using only the information provided by the manufacturer's data sheet. The proposed method presents a computational procedure of low complexity, which makes it possible to estimate the five parameters of any photovoltaic generator. Using the complete equation of the single diode model, the number of parameters to be calculated is reduced only to two parameters by an equation exclusively connecting the series resistance and the diode current. Suitable validations on important case studies are presented; an experimental data from multicrystalline MSX120 and thin film NA-F135 solar panels were used to test the single diode model with the extracted parameters. The experimental data are first collected at the same temperature at two different irradiances levels and at low irradiance level at a fixed temperature for MSX120. In the second stage, variations in temperature are considered at different irradiance level for NA-F135. The extraction results show that the I–V curves accurately fit the entire range of the experimental data. In addition, the results of the proposed procedure are compared to the most recent proposed techniques in literature. Furthermore, the results obtained show a highly accurate; in particular, at maximum power point (MPP), the error is always less than 0.005%, which is quite far of the authorized error of 1%.

scholarly journals Calculating the Energy Yield of Si-Based Solar Cells for Belgium and Vietnam Regions at Arbitrary Tilt and Orientation under Actual Weather Conditions

Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3180
Author(s):  
D. P. N. Nguyen ◽  
Johan Lauwaert
Keyword(s):  
Solar Cells ◽  
Numerical Model ◽  
New Technologies ◽  
Weather Conditions ◽  
Point Of View ◽  
Energy Yield ◽  
Solar Panels ◽  
Major Drawback ◽  
Tandem Solar Cells ◽  
Site Specific

Predicting actual energy harvesting of a photovoltaic (PV) installation as per site-specific conditions is essential, from the customer’s point of view, to choose suitable PV technologies as well as orientations, since most PVs usually have been designed and evaluated under standard illumination. Hence, the tendency lives in the PV community to evaluate the performance on the energy yield and not purely on the efficiency. The major drawback is that weather conditions play an important role, and recording solar spectra in different orientations is an expensive and time-consuming business. We, therefore, present a model to calculate the daily, monthly and annual energy yield of Si-based PV installations included in commercial panels as well as tandem solar cells. This methodology will be used to evaluate the benefit of potential new technologies for domestic and building integrated applications. The first advantage of such a numerical model is that the orientation of solar panels and their properties can be easily varied without extra experiments. The second advantage is that this method can be transferred to other locations since it is based on a minimum of input parameters. In this paper, the energy yield of PV installations for different regions in Belgium and Vietnam will be calculated.

Comparison of Meta-heuristic Algorithms on Benchmark Functions

2019 ◽  
Vol 2 (3) ◽  
pp. 508-517
Author(s):  
FerdaNur Arıcı ◽  
Ersin Kaya
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Time Interval ◽  
Bee Colony ◽  
Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC’17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

MANUFACTURING OF ENERGY EFFICIENT SOLAR PANELS

2020 ◽  
Author(s):  
Андрей Дмитриевич Бухтеев ◽  
Виктория Буянтуевна Бальжиева ◽  
Анна Романовна Тарасова ◽  
Фидан Гасанова ◽  
Светлана Викторовна Агасиева
Keyword(s):  
Energy Efficiency ◽  
Solar Cells ◽  
Energy Efficient ◽  
Solar Panels

В данном обзоре приведены проблемы при использовании солнечных элементов и существующие решения этих проблем по повышению энергоэффективности фотоэлементов. Также сравнивается КПД этих солнечных элементов и рассматриваются их особенности. Одним из самых эффективных способов стало применение нанотехнологий. This review presents the problems of using solar cells and existing solutions to these problems to improve the energy efficiency of solar cells. The efficiency of these solar cells is also compared and their features are considered. One of the most effective methods was the use of nanotechnology.

scholarly journals Device Parameter Extraction for Loss Analysis of Silicon Solar Cells Based on Intelligent Model Fitting

2018 ◽  
Vol 150 ◽  
pp. 21-27 ◽  
Author(s):  
Jian Wei Ho ◽  
Johnson Wong ◽  
Percis Teena Christopher Subhodayam ◽  
Kwan Bum Choi ◽  
Divya Ananthanarayanan ◽  
...  
Keyword(s):  
Solar Cells ◽  
Model Fitting ◽  
Parameter Extraction ◽  
Silicon Solar Cells ◽  
Loss Analysis ◽  
Device Parameter ◽  
Intelligent Model

scholarly journals A Study of Developing a Prediction Equation of Electricity Energy Output via Photovoltaic Modules

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1503
Author(s):  
Minsu Kim ◽  
Hongmyeong Kim ◽  
Jae Hak Jung
Keyword(s):  
Experimental Data ◽  
Real Time ◽  
Air Temperature ◽  
Temperature Difference ◽  
Prediction Accuracy ◽  
Prediction Equation ◽  
Energy Output ◽  
Photovoltaic Modules ◽  
Mean Error ◽  
Pv Module

Various equations are being developed and applied to predict photovoltaic (PV) module generation. Currently, quite diverse methods for predicting module generation are available, with most equations showing accuracy with ≤5% error. However, the accuracy can be determined only when the module temperature and the value of irradiation that reaches the module surface are precisely known. The prediction accuracy of outdoor generation is actually extremely low, as the method for predicting outdoor module temperature has extremely low accuracy. The change in module temperature cannot be predicted accurately because of the real-time change of irradiation and air temperature outdoors. Calculations using conventional equations from other studies show a mean error of temperature difference of 4.23 °C. In this study, an equation was developed and verified that can predict the precise module temperature up to 1.64 °C, based on the experimental data obtained after installing an actual outdoor module.

scholarly journals Prediction of Healing Performance of Autogenous Healing Concrete Using Machine Learning

Materials ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4068
Author(s):  
Xu Huang ◽  
Mirna Wasouf ◽  
Jessada Sresakoolchai ◽  
Sakdirat Kaewunruen
Keyword(s):  
Machine Learning ◽  
Search Algorithm ◽  
Weather Conditions ◽  
Prediction Performance ◽  
Machine Learning Algorithms ◽  
Coefficient Of Determination ◽  
Gradient Boosting ◽  
Support Vector ◽  
Self Healing ◽  
Artificial Neural Network Ann

Cracks typically develop in concrete due to shrinkage, loading actions, and weather conditions; and may occur anytime in its life span. Autogenous healing concrete is a type of self-healing concrete that can automatically heal cracks based on physical or chemical reactions in concrete matrix. It is imperative to investigate the healing performance that autogenous healing concrete possesses, to assess the extent of the cracking and to predict the extent of healing. In the research of self-healing concrete, testing the healing performance of concrete in a laboratory is costly, and a mass of instances may be needed to explore reliable concrete design. This study is thus the world’s first to establish six types of machine learning algorithms, which are capable of predicting the healing performance (HP) of self-healing concrete. These algorithms involve an artificial neural network (ANN), a k-nearest neighbours (kNN), a gradient boosting regression (GBR), a decision tree regression (DTR), a support vector regression (SVR) and a random forest (RF). Parameters of these algorithms are tuned utilising grid search algorithm (GSA) and genetic algorithm (GA). The prediction performance indicated by coefficient of determination (R2) and root mean square error (RMSE) measures of these algorithms are evaluated on the basis of 1417 data sets from the open literature. The results show that GSA-GBR performs higher prediction performance (R2GSA-GBR = 0.958) and stronger robustness (RMSEGSA-GBR = 0.202) than the other five types of algorithms employed to predict the healing performance of autogenous healing concrete. Therefore, reliable prediction accuracy of the healing performance and efficient assistance on the design of autogenous healing concrete can be achieved.

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