Enhancing Solar Cell Performance: Atan-Sinc Optimization Algorithm for Precise Parameter Extraction in the Three-Diode Model

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.

Three-diode lumped-parameter equivalent circuit model conforms to photovoltaic device physics and processes an exact analysis for I- V characteristics of industrial solar cells, because it explains the different leakage current components especially for recombination current resulting from defects or grain boundaries. However, three-diode model's complete circuit topology leads to complicated transcendental I- V equation without analytical solution so that low computation efficiency limits three-diode model's applications in photoelectric simulations. In this article, an effective-diode method is proposed to simplify three-diode model, derive accurate and efficient terminal current-voltage solution to three-diode model, and acquire electrostatic characteristics of the solar cells. The calculated values have good agreements with numerical iteration results and experimental data measured from solar cells, respectively. Finally, the effective-diode method performs an important role of solving three-diode model analytically, predicting I- V characteristics of industrial solar cells accurately, and providing three-diode model's practicability and development in solar cells' simulations.

A new stochastic slime mould optimization algorithm for the estimation of solar photovoltaic cell parameters

An analysis of the industries for which the wide implementation of solar energy is currently considered relevant has been carried out. The aim of the study is to build a methodology for the synthesis of nanostructured pyrazoline dyes that can be used as a coating material for solar cells based on monocrystalline, polycrystalline and amorphous silicon, as well as the development of a system for evaluating the effectiveness and optimization of this methodology according to the target indicators calculated at the quantitative level. In order to build a holistic methodology for the synthesis of photovoltaic coating phosphor, the functions and arguments are defined in the article, on the basis of which the target performance indicators for compensating for the difference in the spectra of solar radiation and absorption of the solar cell substrate are further calculated. Factors for reducing the efficiency of photoelectric converters based on polycrystalline silicon have been determined. As part of the concept of broadening the solar cells absorption spectra by applying the luminophor layer, a method for the synthesis of the nanostructured pyrazoline photoluminescent dyes class is proposed. An experimental study was conducted based on the correlation of the solar radiation spectrum, the absorption spectrum of polycrystalline silicon, as well as the absorption and photoluminescence spectra of synthesized luminophores. A complex mathematical model has been developed for evaluating the effectiveness of the presented technique for expanding the absorption spectra of solar cells and optimizing the specified approach in accordance with the target indicators. The author has considered the procedure of synthesis of nanostructured pyrazoline dye and typical parameters of the procedure that affect the characteristics of this class of phosphors, as well as the corresponding designations. Determination of the general procedure for the synthesis and control of the characteristics of the photoluminescent coating material of the photovoltaic converter, as well as a series of experimental studies allowed the author to identify pyrazoline phosphors that are suitable as a coating material for a photovoltaic converter. Keywords: solar cells, absorption spectrum, photoluminescence spectrum, pyrazoline luminophor, nanostructuring procedure, laser annealing, target functions.

Cast-mono silicon material is interesting for its lower carbon footprint compared to Czochralski (Cz) monocrystalline silicon. However, solar cells fabricated using cast-mono (CM) silicon show lower performances. In this work, two routes to make cast-mono silicon advantageous over Cz silicon are considered. The first route is to further reduce carbon footprint of cast-mono silicon, by using Upgraded Metallurgical Grade silicon (UMG-Si) feedstock instead of Solar Grade silicon (SoG-Si) feedstock. TOPCon solar cells are fabricated using both feedstocks, and cast-mono growth technology, using industrial-type furnaces. Laboratory studies show that UMG-Si can result in efficiencies higher than solar cells made of SoG-Si when feeding the material to a CM crystallization process. But when compared to Cz, CM-UMG-Si TOPCon solar cells conversion efficiency values are still 0.5%abs lower. The second route is to take advantage of the TOPCon passivation layer (e.g., poly-Si) ability to getter metallic impurities, and thus improve the quality of cast-mono material. Several TOPCon sequences are tested and their effect on the carrier recombination properties of the device are studied. In the end, solar cells are fabricated and again, UMG-Si solar cells show better results than SoG-Si solar cells, with efficiency up to 22.65%, independently confirmed.

Photovoltaic technology, which converts the sun’s light energy directly into electricity, can be used to make photovoltaic cells. The use of photovoltaic cells is centered on the idea of a low-carbon economy and green environmental protection, which effectively addresses the pollution problem in smart cities. Accurate identification of photovoltaic cell parameters is critical for battery life cycle and energy utilization. To accurately identify the single diode model (SDM), dual diode model (DDM), and three diode model (TDM) parameters of solar photovoltaic cells, and an improved honey badger algorithm (IHBA) is proposed in this paper. In the early stages of iteration, the IHBA uses the spiral exploration mechanism to improve the population’s global exploration ability. Furthermore, a density update factor that varies according to the quasi-cosine law is introduced to speed up the algorithm’s convergence speed and prevent the algorithm from falling into the local optimal value. Simultaneously, the pinhole imaging strategy is utilized to disturb the present optimal position to improve the algorithm’s optimization accuracy. The experimental comparison results of 18 benchmark test functions, Wilcoxon rank sum statistical test, and 30 CEC2014 test functions reveal that an IHBA shows remarkable performance in convergence speed, optimization accuracy, and robustness. Finally, the IHBA is used to identify the parameters of three kinds of commercial silicon R.T.C French solar photovoltaic cell models with a 57 mm diameter. In comparison to other algorithms, the IHBA can minimize the root mean square error (RMSE) between the measured current and estimated current at the fastest speed, demonstrating the practicality and superiority of the IHBA in tackling this problem.

The main two reasons for the shift to renewable energy are the depletion and the harmful effects of non-renewable energy, such as burning coal and fossil fuels. As a result, the use of solar cells as renewable energy resources, that prioritise high efficiency while lowering production costs, can provide a promising future for sustaining energy resources. Solar cells have been developed starting with the monocrystalline and polycrystalline solar cells (first generation), non-crystalline and thin film solar cells (second generation) and organic solar cells (third generation). The dye-sensitized solar cell (DSSC) is a new type of solar cell that has gained popularity due to its ability to convert energy at a low cost, ease of fabrication, and non-toxic nature. This paper explores the use of fungal colours as natural dyes for DSSC due to their wide range of colours, large yields, sustainable supply and non-toxic. Natural fungal colours can be extracted using proper extraction conditions and methods. Although fungal dyes have several limitations like instability, non-resistance to harsh conditions, colour loss during processing or storage and limited profitable yield, they still have promising future prospects to overcome the poor interaction of natural sensitizer with semiconductor together with other advantages of fungal colour. DSSC with dye from crude extracts from Cortinarius fungi exhibited the highest efficiency of 0.64% among reported DSSC with fungal dye.

In this study, faults in solar panel cells were detected and classified very quickly and accurately using deep learning and electroluminescence images together. A unique and new dataset was created for this study. Monocrystalline and polycrystalline solar panel cells were used in the dataset. The dataset included intact, cracked and broken images for each solar panel cell. The dataset was preprocessed and multiplied to equalize the intact, cracked and broken numbers. Seven different deep learning architectures were used in this study. As a result of this study, 97.82% accuracy was achieved for the monocrystalline solar panel cells and 96.29% for the polycrystalline solar panel cells in the SqueezeNet architecture.

Application of phosphorus diffusion gettering process on upgraded metallurgical grade Si wafers and solar cells

the important issue in the extraction of the unknown parameters in the photovoltaic model is the mathematical models.Therefore, a five equivalent circuit is analyzed in this work.The unknown parameters are five for single diode model, seven for double-diode model, eight for modified double-diode, and nine for three-diode model and ten for modified three-diode parameters.The performance of the modified three-diode model and three-diode model after estimate all parameters without assumption for any variable are compared with the performance of the both single diode model, double-diode model and also the modified double-diode of the same solar cell.Cuckoo search optimization algorithm (CSOA) is proposed for the estimation of the unknown parameters for the five solar cell equivalent models.The root mean square error, the absolute error for current and power, and the maximum power point absolute error are analyzed to the performance of the five solar cell models.

In this study, Newton–Raphson Based (NRB) scalar speed control (SSC) is recommended to control the speed of Induction Motors (IM). As is known, the Newton–Raphson method, which is often used in numerical analysis, is one of the methods that provide a rapid convergence of the desired value. In this study, the frequency value required for the desired speed value is calculated by Newton–Raphson method. Three different models including Newton–Raphson Based + Scalar Speed Control (NRB_SSC), Difference Speed + NRB_SSC (DS_NRB_SSC), DS + NRB_SSC + Difference Frequency (DS_NRB_SSC_DF) were used for optimization tests. Simulation of the work was done in Matlab/Simulink programme. According to obtained test results DS_NRB_SSC_DF has produced better results.

SCHOTT Solar, DE.E-mail: sinke@ecn.nl, Project website: www.ipcrystalclear.info.ABSTRACT: CrystalClear was an EU co-financed Integrated Project aimed at developing technology for wafer-based silicon solar modules at 1 € per watt-peak manufacturing costs and a strongly improved environmental profile. The project consortium has selected a number of technologies that potentially comply with these aims as well as research that serves as basis for further developments (beyond the project aims). These technologies have been demonstrated in the form of demonstrators, i.e. full-size modules featuring all innovations necessary to comply with the aims. The project has shown that wafer-based multicrystalline-silicon solar modules can be produced at 1 € per watt-peak at a world-record efficiency of 16% and an energy pay-back time of less than 2 years in Southern Europe.Keywords: back contact, c-Si, cost reduction, manufacturing and processing, module manufacturing, multicrystalline silicon, silicon solar cell, LCA, energy pay-back time.1 GENERAL PROJECT OVERVIEWCrystalClear [1-4] was a 5½-year Integrated Project carried out in the 6th Framework Program of the EU. It started in January 2004 and was finished in June 2009. The project was a joint effort of a consortium of 16 European companies, research institutes and university groups involved in wafer-based crystalline silicon PV technology.Companies: BP Solar (ES), Deutsche Cell (DE), Deutsche Solar (DE), Isofoton (ES), Photowatt (FR), REC (NO), REC Wafer Norway (NO), SCHOTT Solar (DE), SolarWorld Industries (DE).Universities: Utrecht (NL), Konstanz (DE), UPM-IES (ES);Research institutes: InESS-CNRS (FR), ECN (NL, project coordinator), FhG-ISE (DE), IMEC (BE).Within CrystalClear an average of over 50 researchers from 6 European countries have worked together.The project aims have been divided in three main blocks.1. Availability of innovative manufacturing technologies which allow solar modules to be produced at a cost of 1 €/watt-peak (which is a reduction by more than 50% compared to state-of-the-art at the start of the project). This objective is very ambitious, but essential to get world-class technology. Manufacturing cost reduction is essential to bring prices of modules and turn-key complete systems down.2. Improved environmental profile of solar modules by reduction of materials consumption, replacement of undesired materials and designing for recycling. This will strengthen the position of solar energy as a clean and sustainable alternative to conventional electricity generation.3. Enhanced applicability of modules by tailoring to customer needs and by improving product lifetime and reliability. Since solar modules will be used in very different situations (e.g. on buildings) flexibility of use is crucial. Assured quality is a prerequisite for large-scale, professional use.The CrystalClear project has tackled all aspects from the raw materials up to the completed solar module. Key activities concerned:• strongly reducing the consumption of expensive materials (especially silicon, but also others) as well as introducing the use of cheaper materials;• increasing the electricity output of solar modules;• developing highly automated, high-throughput, low-cost manufacturing processes;• screening materials, processes and products in relation to sustainability and suitability for large-scale use.Since many combinations of options for cell and module design, processing and materials potentially fulfill the project aims, a selection of 6 distinctly different overall technologies has been developed, underlining the many faces of wafer-based silicon PV and the variety of approaches found within the industry. The feasibility of these technologies to comply with the projects aims has been verified by detailed cost and environmental analyses. CrystalClear has chosen technologies which might be demonstrated as full-scale modules already at the end of the project as well as technologies which stillneed to be developed further after the project.The project has been organized in Subprojects (SPs), covering the parts of the value chain as well as integrating aspects (environmental analyses, cost calculations, etc.), see Figure 1. In the following, the final project results achieved are summarized per Subproject:

There is a need for an accurate method for modeling the Photovoltaic (PV) cell, because the modeling of PV cell is based on manufacturer datasheet. The manufacturer datasheet is recorded under standard test condition (STC) and under laboratory conditions, especially the PV cell temperature, where the temperature used in modeling is ambient temperature, but in the real operation of a PV cell, there are huge differences between ambient temperature and PV cell temperature. Therefore, in this paper a novel approach is used for modeling a PV array by the use of back of panel temperature and real life recorded data (outdoor data). The developed model gives a real life operation condition according to the simulation results, which is compared with validation data (real life data).

The critical worldwide revolution towards clean energy has prompted the improvement of studies on the fabrication of high-performance solar cells. In this regard, rendering an accurate model of the solar cell for performance evaluation in the simulation could be essential. So far, several models have been proposed for the solar cell, including single-diode model (SDM), double-diode model (DDM), and three-diode model (TDM). By increasing the number of diodes considered in the equivalent circuit in order to deliver a more accurate model, the number of unknown parameters which must be identified will be increased as well. Therefore, presenting an efficient algorithm to estimate these parameters becomes an interesting issue in recent years. In this study, an improved optimization algorithm, called springy whale optimization algorithm (SWOA), is proposed to estimate the model parameters of solar cells. SWOA is a generalization of the WOA and has the advantages of high convergence speed, global search capability, and high robustness over it. In order to inquire the efficiency of SWOA, this algorithm is posed to estimate the parameters of models of solar cells and photovoltaic (PV) modules as well; the simulation results authenticate the supremacy of the proposed algorithm. Furthermore, the effectiveness of SWOA algorithm in the practical application has been evaluated using commercial modules, including polycrystalline (SW255), multi-crystalline (KC200GT), and monocrystalline (SM55). This assessment is carried out for various operating conditions under different irradiance and temperature conditions, which yield variations in the parameters of the PV model. The results obtained from various experimental setups confirm the high performance and robustness of the proposed algorithm.

Analysis of variation in recombination characteristics due to light and heat in industrial silicon solar cells