Mono-crystalline Cells Research Articles

Design and Simulation of a Solar Powered House in Muğla: Roof Decorated with Half Cut Cells

In this article, the performance, design and efficiency calculation of photovoltaic systems were made using PvSol program. For roof type panel design close to standard installations, panel position and panel type were designed based on Muğla province sun hour data and roof shading factor. In this study, the data obtained for the roof type installation designed in Muğla city using monocrystalline, polycrystalline and half-cut monocrystalline cells were compared. When yield, area and price parameters were compared, it was found that the best cell type was half-section monocrystalline. As a result of the simulations, it was found that the average daily energy need of the house is 6.4kWh, fed by 14 half-section monocrystalline panels. The results obtained are consistent with the data in the literature.

An Experimental Investigation of Back Flow Cooling Tempered Glass Photovoltaic Thermal System in the Malaysian Peninsula Climate

Solar photovoltaic (PV) technology transforms sunlight into electricity and is expected to play a significant role in Malaysia’s renewable energy growth. However, as the temperature of the cell rises, so does its electrical efficiency. Photovoltaic thermal (PV/T) systems combine a PV system with a solar thermal collector (STC) to capture heat energy from solar cells and boost their efficiency. In this study, back flow thermal absorbers PV/T were designed utilizing cooper pipe, and the systems were constructed using monocrystalline cells, tempered glass, and epoxy lamination. Various designs of spiral, horizontal, and vertical pipes were used. The experiment was carried out between December and January at Wisma R&D, University of Malaya, with real-time data recording on power output, water, and surface temperatures at the peak sun irradiation of 735 W/m2 in Kuala Lumpur, Malaysia. The spiral PV/T system had the maximum daily electrical efficiency of 15.2%, compared to horizontal PV/T, vertical PV/T at a flow rate of 0.5 LPM, and PV system-only, which had daily electrical efficiencies of 14.4%, 14.36%, and 13.65%, respectively. The spiral PV/T system also had the highest daily thermal efficiency of 23.78%, compared to the horizontal and vertical PV/T systems, which had 23.02% and 13.95%, respectively. When compared to a PV system, the PV/T system increased the electrical efficiency of the module, with the spiral design having the highest efficiency among the designs.

Effect of Protective Layer on the Performance of Monocrystalline Silicon Cell for Indoor Light Harvesting.

The development of renewable energy sources has grown increasingly as the world shifts toward lowering carbon emissions and supporting sustainability. Solar energy is one of the most promising renewable energy sources, and its harvesting potential has gone beyond typical solar panels to small, portable devices. Also, the trend toward smart buildings is becoming more prevalent at the same time as sensors and small devices are becoming more integrated, and the demand for dependable, sustainable energy sources will increase. Our work aims to tackle the issue of identifying the most suitable protective layer for small optical devices that can efficiently utilize indoor light sources. To conduct our research, we designed and tested a model that allowed us to compare the performance of many small panels made of monocrystalline cells laminated with three different materials: epoxy resin, an ethylene-tetrafluoroethylene copolymer (ETFE), and polyethylene terephthalate (PET), under varying light intensities from LED and CFL sources. The methods employed encompass contact angle measurements of the protective layers, providing insights into their wettability and hydrophobicity, which indicates protective layer performance against humidity. Reflection spectroscopy was used to evaluate the panels' reflectance properties across different wavelengths, which affect the light amount arrived at the solar cell. Furthermore, we characterized the PV panels' electrical behavior by measuring short-circuit current (ISC), open-circuit voltage (VOC), maximum power output (Pmax), fill factor (FF), and load resistance (R). Our findings offer valuable insights into each PV panel's performance and the protective layer material's effect. Panels with ETFE layers exhibited remarkable hydrophobicity with a mean contact angle of 77.7°, indicating resistance against humidity-related effects. Also, panels with ETFE layers consistently outperformed others as they had the highest open circuit voltage (VOC) ranging between 1.63-4.08 V, fill factor (FF) between 35.9-67.3%, and lowest load resistance (R) ranging between 11,268-772 KΩ.cm-2 under diverse light intensities from various light sources, as determined by our results. This makes ETFE panels a promising option for indoor energy harvesting, especially for powering sensors with low power requirements. This information could influence future research in developing energy harvesting solutions, thereby making a valuable contribution to the progress of sustainable energy technology.

Parameter Estimation Techniques for Photovoltaic System Modeling

In improving PV system performance, the parameters associated with electrical photovoltaic equivalent models play a pivotal role. However, due to the increased mathematical complexities and non-linear traits of PV cells, the precise prediction of these parameters is a challenging task. To estimate the parameters associated with PV models, a reliable, robust, and accurate optimization technique is needed. This paper introduces a new algorithm, Rat Swarm Optimizer (RSO), for obtaining the optimum PV cell and module parameters. The proposed method maintains an adequate balance between the exploration and exploitation phases to overcome premature particle issues. The results obtained using RSO are compared with those of other algorithms, i.e., Particle Swarm Optimization (PSO), Ant Lion Optimizer (ALO), Salp Swarm Algorithm (SSA), Harris Hawks Optimization (HHO), and Grasshopper Optimization (GOA), in this work. The modified one-diode model (MODM) and modified two-diode model (MTDM) are used to analyze the parameters of the mono-crystalline PV cell using the suggested RSO. The obtained findings imply that the parameters estimated by the suggested RSO are more accurate than those calculated by the other algorithms taken into consideration in the paper. The statistical results are compared, and it is clear that RSO is a very accurate, fast, and dependable approach for the parameter estimation of PV cells.

Anti-sine-cosine atom search optimization (ASCASO): a novel approach for parameter estimation of PV models.

Nowadays, solar power generation has gradually become a part of electric energy sharing. How to effectively enhance the energy conversion efficiency of solar cells and components has gradually emerged as a focal point of research. This paper presents a boosted atomic search optimization (ASO) with a new anti-sine-cosine mechanism (ASCASO) to realize the parameter estimation of photovoltaic (PV) models. The anti-sine-cosine mechanism is inspired by the update principle of sine cosine algorithm (SCA) and the mutation strategy of linear population size reduction adaptive differential evolution (LSHADE). The working principle of anti-sine-cosine mechanism is to utilize two mutation formulas containing arcsine and arccosine functions to further update the position of atoms. The introduction of anti-sine-cosine mechanism achieves the populations' random handover and promotes the neighbors' information communication. For better evaluation, the proposed ASCASO is devoted to estimate parameters of three PV models of R.T.C France, one Photowat-PWP201 PV module model, and two commercial polycrystalline PV panels including STM6-40/36 and STM6-120/36 with monocrystalline cells. The proposed ASCASO is compared with nine reported comparative algorithms to assess the performance. The results of parameter estimation for different PV models of various methods demonstrate that ASCASO performs more accurately and reliably than other reported comparative methods. Thus, ASCASO can be considered a highly effective approach for accurately estimating the parameters of PV models.

Influence of photovoltaic cell technologies and elevated temperature on photovoltaic system performance

This work deploys a configured hypothetical 6-kWp capacity PV system, with mounted rooftop panels, to examine the performance of a PV system, corresponding to different (a) PV cell technologies; (b) ambient temperatures. The first (crystalline silicon (c-Si)) and second (copper indium gallium selenide (CIGS)) generations of PV cells have been chosen for this study. A range of ambient temperatures, −10 °C to 50 °C, at an interval of 5 °C, will be used to investigate the influence of temperature on PV system performance, using the chosen PV cells. A PV design and simulation software, PVsyst, was deployed based on optimisation considerations. It was observed from the report that: the CIGS PV cell produced the highest energy at standard tests and conditions (STC) and elevated temperature; the Si mono and polycrystalline yielded the same amount of energy; the CIGS and Si monocrystalline cells PV have the highest and lowest PR, respectively.

Effective transfer learning of defect detection for photovoltaic module cells in electroluminescence images

Due to the damage during production, transportation and installation, some defects inevitably occur in the solar cells, which will reduce the power generation efficiency. Benefiting from the development of deep learning, the performance of solar cell defect detection has been improved by a considerable margin. However, a problem persists that a model trained on one batch of data may not work on another batch following different production schemes. Moreover, compared with monocrystalline cells, more impurities on the surface of polycrystalline cells make it more difficult to inspect defects and obtain labels. To address this problem, we formulate it as a monocrystalline to polycrystalline task based on an unsupervised domain adaptation task in transfer learning. A standard network with CNN architecture is adopted as our classifier, which can tell whether the solar cell is cracked. Based on that, we embed adversarial learning in the network, where the domain discriminator is added to distinguish data domains, so that the feature extractor can learn domain-invariant features. Considering the scenario of defect detection in Electroluminescence (EL) images, we further propose attention-based transfer learning and class-aware domain discriminator to enhance the effect of knowledge transfer. When detecting a batch of new polycrystalline products, our method can improve F1-score by 0.2631, and the recall and precision can reach 84.70% and 90.15%, respectively.

Experimental Studies for Glass Light Transmission Degradation in Solar Cells Due to Dust Accumulation Using Effective Optical Scattering Parameters and Machine Learning Algorithm

Environmental impacts influence solar cell performance significantly. A harsh environment may cause temperature, wind speed, or humidity uncertainties. In the case of photovoltaic systems in desert or semidesert areas, dust accumulation critically impacted solar cell degradation. Herein, we developed a novel machine-learning-based optical model that estimates solar cell efficiency's degradation because of dust accumulation. Glass substrates were directed to dust for 16 weeks with a weekly characterization procedure. Samples were morphologically and optically characterized to be seeded into the scattering model. Morphological characterization measurements were conducted using SEM and EDX-mapping to explore the internal composition of dust particles. Optically, the dusty glass substrate sample's transmission spectra were measured using a UV-Vis spectrometer. Refractive index, thickness, and scattering particle radius have been extracted as effective parameters with fitting accuracy of 95%. The extracted dataset is then inputted into a machine-learning model to predict the transparency degradation in the glass substrate. Finally, predicted data is validated against the actual degraded monocrystalline cell, showing 89.4% of matching.

Performance monitoring of different types of photovoltaic cells

Scientific and technological developments make major changes in the energy sector for both generating energies from sustainable energy sources and monitoring of energy systems in ways that are more efficient. The main purpose of this study is to measure experimental data such as temperature, current and voltage by way of a sensor-based microcontroller device. Ergo, Arduino Uno is used for an ATmega328 microcontroller, voltage and current sensors are used to receive electric data that solar cells convert from solar radiation and a temperature and humidity sensor is used to collect weather data. The air temperature and solar radiation values have been taken from the Copernicus Atmosphere Monitoring Service. To store data, a secure digital memory card module (SD) is used. The performance evaluation of different types of photovoltaic cells, which are monocrystalline and polycrystalline cells, is aimed at in this study. To harvest energy, four similar monocrystalline and four similar polycrystalline cells are used. Monitoring the performance of each solar cell is the other objective of this study. Results of the performance of different types of solar cells are as expected, the starting point for further studies both for monitoring and for evaluating the performance of solar cells and sensor-based devices.

Solar Energy Production in India and Commonly Used Technologies—An Overview

This review uses a more holistic approach to provide comprehensive information and up-to-date knowledge on solar energy development in India and scientific and technological advancement. This review describes the types of solar photovoltaic (PV) systems, existing solar technologies, and the structure of PV systems. Substantial emphasis has been given to understanding the potential impacts of COVID-19 on the solar energy installed capacity. In addition, we evaluated the prospects of solar energy and the revival of growth in solar energy installation post-COVID-19. Further, we described the challenges caused by transitions and cloud enhancement on smaller and larger PV systems on the solar power amended grid-system. While the review is focused on evaluating the solar energy growth in India, we used a broader approach to compare the existing solar technologies available across the world. The need for recycling waste from solar energy systems has been emphasized. Improved PV cell efficiencies and trends in cost reductions have been provided to understand the overall growth of solar-based energy production. Further, to understand the existing technologies used in PV cell production, we have reviewed monocrystalline and polycrystalline cell structures and their limitations. In terms of solar energy production and the application of various solar technologies, we have used the latest available literature to cover stand-alone PV and on-grid PV systems. More than 5000 trillion kWh/year solar energy incidents over India are estimated, with most parts receiving 4–7 kWh/m2. Currently, energy consumption in India is about 1.13 trillion kWh/year, and production is about 1.38 trillion kWh/year, which indicates production capacities are slightly higher than actual demand. Out of a total of 100 GW of installed renewable energy capacity, the existing solar capacity in India is about 40 GW. Over the past ten years, the solar energy production capacity has increased by over 24,000%. By 2030, the total renewable energy capacity is expected to be 450 GW, and solar energy is likely to play a crucial role (over 60%). In the wake of the increased emphasis on solar energy and the substantial impacts of COVID-19 on solar energy installations, this review provides the most updated and comprehensive information on the current solar energy systems, available technologies, growth potential, prospect of solar energy, and need for growth in the solar waste recycling industry. We expect the analysis and evaluation of technologies provided here will add to the existing literature to benefit stakeholders, scientists, and policymakers.

Forecasting of Photovoltaic Power by Means of Non-Linear Auto-Regressive Exogenous Artificial Neural Network and Time Series Analysis

In this research paper, a nonlinear autoregressive with exogenous input (NARX) model of the nonlinear system based on neural network and time series analysis is proposed to deal with the one-month forecast of the produced power from photovoltaic modules (PVM). The PVM is a monocrystalline cell with a rated production of 175 watts that is placed at Heliopolis University, Bilbéis city, Egypt. The NARX model is considered powerful enough to emulate the nonlinear dynamic state-space model. It is extensively performed to resolve a variety of problems and is mainly important in complex process control. Moreover, the NARX method is selected because of its quick learning and completion times, as well as high appropriateness, and is distinguished by advantageous dynamics and interference resistance. The neural network (NN) is trained and optimized with three algorithms, the Levenberg–Marquardt Algorithm (NARX-LMA), the Bayesian Regularization Algorithm (NARX-BRA) and the Scaled Conjugate Gradient Algorithm (NARX-SCGA), to attain the best performance. The forecasted results using the NARX method based on the three algorithms are compared with experimentally measured data. The NARX-LMA, NARX-BRA and NARX-SCGA models are validated using statistical criteria. In general, weather conditions have a significant impact on the execution and quality of the results.

Anomaly Detection and Automatic Labeling for Solar Cell Quality Inspection Based on Generative Adversarial Network

Quality inspection applications in industry are required to move towards a zero-defect manufacturing scenario, with non-destructive inspection and traceability of 100% of produced parts. Developing robust fault detection and classification models from the start-up of the lines is challenging due to the difficulty in getting enough representative samples of the faulty patterns and the need to manually label them. This work presents a methodology to develop a robust inspection system, targeting these peculiarities, in the context of solar cell manufacturing. The methodology is divided into two phases: In the first phase, an anomaly detection model based on a Generative Adversarial Network (GAN) is employed. This model enables the detection and localization of anomalous patterns within the solar cells from the beginning, using only non-defective samples for training and without any manual labeling involved. In a second stage, as defective samples arise, the detected anomalies will be used as automatically generated annotations for the supervised training of a Fully Convolutional Network that is capable of detecting multiple types of faults. The experimental results using 1873 Electroluminescence (EL) images of monocrystalline cells show that (a) the anomaly detection scheme can be used to start detecting features with very little available data, (b) the anomaly detection may serve as automatic labeling in order to train a supervised model, and (c) segmentation and classification results of supervised models trained with automatic labels are comparable to the ones obtained from the models trained with manual labels.

On the Determination of the Output Power in Mono/Multicrystalline Photovoltaic Cells

In the present work, two artificial intelligence-based models were proposed to determine the output power of two types of photovoltaic cells including multicrystalline (multi-) and monocrystalline (mono-). Adaptive neuro-fuzzy inference system (ANFIS) and Least-squares support vector machine (LSSVM) are applied for the output power calculations. The estimation results are very close to the actual data based on graphical and statistical analysis. The coefficients of determination ( R 2 ) of monocrystalline cell output power for LSSVM and ANFIS models are as 0.997 and 0.962, respectively. Additionally, multicells have R 2 values of 0.999 and 0.995 for LSSVM and ANFIS, respectively. The acceptable values for R 2 and various error parameters prove the accuracy of suggested models. The visualization of these comparisons clarifies the accuracy of suggested models. Additionally, the proposed models are compared with previously published machine learning methods. The accurate performance of proposed models in comparison with others showed that our models can be helpful tools for the estimation of output power. Moreover, a sensitivity analysis for the effects of inputs parameters on output power has been employed. The sensitivity output shows that light intensity has more on output power. The outcomes of this study provide interesting tools which have potential to apply in different parts of renewable energy industries.

Characterization of Solar Cell by varying illumination

This work aims to confirm that the single-diode model of a monocrystalline cell can be used successfully also for thin-film solar cells when their basic parameters are calculated using the method of changing illumination. The authors describe the experimental procedure, in which I–V curves of thin-film solar cells are measured when illumination levels are changing. In experiments, a fully reflective 500 W solar simulator is used as an excellent broadband white light source, which provides the range of 0.01–10 Sun settings.

Monofacial and Bifacial Micro PV Installation as Element of Energy Transition—The Case of Poland

The several government subsidies available in Poland contributed to an increased interest in PV installations. Installed PV capacity increased from 100 MW in 2016 up to 2682.7 MW in July 2020. In 2019 alone, 104,000 microinstallations (up to 50 kWp) were installed in Poland. The paper determines the energy gain and the associated reduction of CO2 emissions for two types of solar installation located in Poland. The monofacial solar modules with a power of 5.04 kWp (located in Leki) and bifacial solar modules with a power of 6.1 kWp (located in Bydgoszcz). Both installations use mono-crystalline Si-based 1st generation PV cells. With comparable insolation, a bifacial installation produces approx. 10% (for high insolation) to 28% (for low insolation) more energy than a monofacial PV installation. Avoided annual CO2 emission in relation to the installation capacity ranges from 0.58 to 0.64 Mg/kWp for monofacial and from 0.68 to 0.74 Mg/kWp for bifacial and is on average approx. 16% higher for bifacial installations. Cost-benefit analyses were made. For different electricity prices, the NPV for monofacial and bifacial was determined.

Photovoltaic panel type influence on the performance degradation due dust accumulation

This study investigates the effect of the type of solar cell technology used on its outcomes degradation when dust accumulates on it. Therefore, in this study, practical tests were performed on the physical properties of accumulated dust in the University of Technology Campus-Iraq for a period of three months. After that, a practical study examined the effect of the type of photovoltaic unit used on the decrease in current, voltage, and energy resulting from the accumulation of this dust with specific mass fractions. The comparison was made between the Amorphous, Monocrystalline, Polycrystalline, and Organic PV modules. The accumulation of dust causes significant decreases in current and has a limited effect on the voltages of all modules studied, as a result, the electric power generated decrease from all the studied photovoltaic modules due to the accumulation of dust. The accumulation of dust had the greatest effect on the Amorphous PV cell, followed by polycrystalline, monocrystalline and finally organic at 42%, 36.3%, 32.9% and 25.7%, respectively. The study demonstrated that the best option is to use monocrystalline cells for the university site. The study also concluded that the choice of any PV technology for a station at any site should be preceded by a study of the effect of dust in that location in order to favor any technique that must be applied. Keywords: Dust accumulation; Amorphous, polycrystalline, monocrystalline; organic; PV module

Thermal analysis of water-cooled photovoltaic cell by applying computational fluid dynamics

Photovoltaic (PV) modules are used for producing electricity in different scales. The efficiency of these modules is highly dependent on their temperature. Owing to the higher efficiency of PV cells at lower temperatures, different thermal management approaches have been suggested in recent years. Water cooling is one of the most efficient methods in PV cells thermal management. In the present article, numerical simulation, on the basis of computational fluid dynamic, is performed to investigate the influence of solar irradiance, ambient temperature and speed of wind on the efficiency and temperature of a monocrystalline cell. The outcomes of simulation revealed that utilizing the applied cooling approach for PV cells in hot ambient temperature and high solar irradiance is more useful. In addition, it is concluded that by employing water for cooling the cell, under the considered conditions, the temperature of the cell can be kept in an appropriate range which prevents its efficiency degradation at high solar irradiance and ambient temperatures. Furthermore, according to the obtained temperature of the cell in different considered cases it is observed that the effect of wind speed is in lower degree of importance compared with solar irradiance and ambient temperature in cases of employing water cooling, while wind speed impact become noticeable when the thermal management approach is not employed. The maximum enhancement in the cell efficiency is approximately 52% in comparison with the reference case without applying cooling.

Power Loss of Crystalline Solar Cell under Cyclic Loadings

In application of building attached photovoltaic in China, the solar cells in the modules are mono-crystalline or polycrystalline cells. In some applications where the wind load is at higher level, a large deflection of the modules will occur. Due to the fluctuation of the wind load, the initial damages inside the cells will develop under this cyclic loading, as a result, the power loss of the solar cell can be observed due to the development of the fatigue damage. In this paper, the fatigue damage evolution of crystalline solar cell in module is investigated experimentally. The damage evolution with the cyclic number was obtained via the measurement of the power loss of the cell. The damage evolution equation was derived based on the continuum damage mechanics and the fatigue life of the cell was predicted.

Exergy, economic and environmental evaluation of an optimized hybrid photovoltaic-geothermal heat pump system

This research presents different scenarios of hybrid photovoltaic-geothermal heat pump system that can provide the requirements of the heating and cooling load consumption in a residential building during the year. One of the major purposes of the present study is to explore the optimum contribution of the solar and ground source energies in which the generation energy costs are reasonable with respect to the total costs, lifetime, and energy losses. For this purpose, a numerical dynamic model consisting of different solar photovoltaic (PV) panels of polycrystalline, monocrystalline, and thin-film cells, batteries, inverter, and ground source heat pump (GSHP) was developed using the engineering equation solver and TRNSYS software. In order to reach the best performance of the system, multi- and single-objective optimizations of the life cycle cost and irreversibility were carried out using the particle swarm optimization algorithm considering all effective parameters of the system including surface area, slope, direction of PV panels and battery capacity. Moreover, the optimization tool which was developed in MATLAB software, was adopted for data interaction between TRNSYS and MATLAB. The results indicate that among various scenarios, the optimized hybrid polycrystalline PV system with an area of 35 m2 and a solar fraction of 31% can be cost-effective up to a 24% inflation rate. Also, the payback period of the optimized hybrid system compared to the regular GSHP is 3 years with the average global costs, while this value is 10 years for Iran’s economic situation.

Formulation of an efficiency correlation for mono-crystalline PV modules using dimensional analysis

Dimensional analysis methodology was applied to I-V data of PV modules and analyzed to relate the parameters defining conversion of photovoltaic power. A single equation model was developed, relating normalized efficiency to normalized cell temperature and irradiance. The developed model was validated on reported data of different module makes of mono-crystalline cells. The error average was around 1%. The study revealed that the effect of normalized temperature change on normalized maximum efficiency is identical for all mono-crystalline cell makes. The influence of irradiance on efficiency was shown to depend on the module’s specific technology.