Comprehensive Examination of Solar Panel Design: A Focus on Thermal Dynamics

In the 21st century, the deployment of ground-based Solar Photovoltaic (PV) Modules has seen exponential growth, driven by increasing demands for green, clean, and renewable energy sources. However, their usage is constrained by certain limitations. Notably, the efficiency of solar PV modules on the ground peaks at a maximum of 25%, and there are concerns regarding their long-term reliability, with an expected lifespan of approximately 25 years without failures. This study focuses on analyzing the thermal efficiency of PV Modules. We have investigated the temperature profile of PV Modules under varying environmental conditions, such as air velocity and ambient temperature, utilizing Computational Fluid Dynamics (CFD). This analysis is crucial as the efficiency of PV Modules is significantly impacted by changes in the temperature differential relative to the environment. Furthermore, the study highlights the effect of airflow over solar panels on their temperature. It is found that a decrease in the temperature of the PV Module increases Open Circuit Voltage, underlining the importance of thermal management in optimizing solar panel performance.

Automated Classification of Snow-Covered Solar Panel Surfaces Based on Deep Learning Approaches

Recently,the demand for renewable energy has increased due to its environmental and economic needs.Solar panels are the mainstay for dealing with solar energy and converting it into another form of usable energy.Solar panels work under suitable climatic conditions that allow the light photons to access the solar cells,as any blocking of sunlight on these cells causes a halt in the panels work and restricts the carry of these photons.Thus,the panels are unable to work under these conditions.A layer of snow forms on the solar panels due to snowfall in areas with low temperatures.Therefore,it causes an insulating layer on solar panels and the inability to produce electrical energy.The detection of snow-covered solar panels is crucial,as it allows us the opportunity to remove snow using some heating techniques more efficiently and restore the photovoltaics system to proper operation.This paper presents five deep learning models,■-16,■-19,ESNET-18,ESNET-50,and ESNET-101,which are used for the recognition and classification of solar panel images.In this paper,two different cases were applied;the first case is performed on the original dataset without trying any kind of preprocessing,and the second case is extreme climate conditions and simulated by generating motion noise.Furthermore,the dataset was replicated using the upsampling technique in order to handle the unbalancing issue.The conducted dataset is divided into three different categories,namely;all_snow,no_snow,and partial snow.The fivemodels are trained,validated,and tested on this dataset under the same conditions 60%training,20%validation,and testing 20%for both cases.The accuracy of the models has been compared and verified to distinguish and classify the processed dataset.The accuracy results in the first case showthat the comparedmodels■-16,■-19,ESNET-18,and ESNET-50 give 0.9592,while ESNET-101 gives 0.9694.In the second case,the models outperformed their counterparts in the first case by evaluating performance,where the accuracy results reached 1.00,0.9545,0.9888,1.00.and 1.00 for■-16,■-19,ESNET-18 and ESNET-50,respectively.Consequently,we conclude that the second case models outperformed their peers.

Indian Solar Panel Initiatives in Reducing Carbon Dioxide Emissions

Environmental degradation and the emission of greenhouse gases particularly carbon dioxide have expanded problems to human wellness and to the atmosphere. The second-most populated country in the globe, India, is among the primary users of conventional resources, which leads to global warming. The growth rate is anticipated to raise more before 2050, which will cause the brisk industrial expansion and rising energy demand to both increases. In order to reduce carbon emissions and meet energy requirements, many countries use alternate usage of renewable energy particularly solar energy. In this review we aim to study solar panel schemes initiated by India, mainly focusing on National Solar Mission. This study also reviews the present solar installed capacity, solar panel scheme 2022, and initiatives and outcomes of solar panels in residences and offices. This study reviewed that by using solar panel resources, the (MNRE) Ministry of New and Renewable Energy hopes to help the Indian Government reach its purpose of 100 GW solar installed capacity by end of 2022. Despite having an amazing 40 GW of solar power installed capacity till December 2021, India is still far from reaching its own goal of 100 GW by March 2023 as per NSM. In essence, this means that India will need to change a few of its ongoing plans further.

Experimental Processus for Acquisition Automatic Features of I-V Properties and Temperature of the Solar Panel by Changing the Operating Point

A development of an acquisition of the characteristic of a solar panel by automatic load variation system is put into play and coupled to an instrumentation chain for taking account of temperature. A programmed digital microprocessor control enables this automation. Design and implementation of a device for automation of variations of the resistive load are powered by solar panel. It is provided by a PIC 16F877A running a computer program that we have developed on the basis of an algorithm according to the operation that we have set. By varying automatically the resistive load, we were able to automatically acquire the characteristic I-V and temperature of the solar panel. With automatic combinations of the 10 resistors, we have obtained 1024 measures of the characteristic curve of the solar cell which has a good accuracy. The change in load and temperature measurement allows us to have the characteristic curves parameterized by temperature.

Simulation and Analysis of Flexible Solar Panels' Deployment and Locking Processes

To predict the attitude of satellite during the whole deployment process and evaluate the locking impact, a numerical flexible model of a certain satellite associated with four flexible honeycomb solar panels was established. The flexible solar panel was modeled by the finite element analysis (FEA), and the motion equations were derived by Lagrangian formulation. The locking process was based on the method of Hertzian contact, which enables one to predict the locking impact on the satellite and the subsequent oscillation of solar panels. The results reveal that locking operation has great impact on the attitude of the satellite, and the angular acceleration of satellite reaches 22.03°/s2 at the locking moment; the flexible solar panels model is feasible to predict the accurate response of the satellite during deployment and the oscillation of solar panels; the instantly impulsive force occurred during locking process is about 1.5 kN and the changing time is nearly 0.32 s. It provides an effective approach to present the flexible solar panels' deployment process and evaluate the locking impact.

Experimental Investigation of Solar Panel Cooling by a Novel Micro Heat Pipe Array

A novel micro heat pipe array was used in solar panel cooling. Both of air-cooling and water-cooling conditions under nature convection condition were investigated in this paper. Compared with the ordinary solar panel, the maximum difference of the photoelectric conversion efficiency is 2.6%, the temperature reduces maximally by 4.7℃, the output power increases maximally by 8.4% for the solar panel with heat pipe using air-cooling, when the daily radiation value is 26.3 MJ. Compared with the solar panel with heat pipe using air-cooling, the maximum difference of the photoelectric conversion efficiency is 3%, the temperature reduces maximally by 8℃, the output power increases maximally by 13.9% for the solar panel with heat pipe using water-cooling, when the daily radiation value is 21.9 MJ.

Dynamic modeling of spacecraft solar panels deployment with Lie group variational integrator

The spacecraft with multistage solar panels have nonlinear coupling between attitudes of central body and solar panels, especially the rotation of central body is considered in space. The dynamics model is based for dynamics analysis and control, and the multistage solar panels means the dynamics modeling will be very complex. In this research, the Lie group variational integrator method is introduced, and the dynamics model of spacecraft with solar panels that connects together by flexible joints is built. The most obvious character of this method is that the attitudes of central body and solar panels are all described by three-dimensional attitude matrix. The dynamics models of spacecraft with one and three solar panels are established and simulated. The study shows Lie group variational integrator method avoids parameters coupling and effectively reduces difficulty of modeling. The obtained continuous dynamics model based on Lie group is a set of ordinary differential equations and equivalent with traditional dynamics model that offers a basis for the geometry control.

Uniform Exponential Stabilization for Flexural Vibrations of a Solar Panel

Here we study a problem of stabilization of the flexural vibrations or transverse vibrations of a rectangular solar panel. The dynamics of vibrations is governed by the fourth order Euler-Bernoulli beam equation. One end of the panel is held by a rigid hub and other end is totally free. Due to attachment of the hub, its dynamics leads to a non-standard equation. The exponential stabilization of the whole system is achieved by applying an active boundary control force only on the rigid hub. The result of uniform stabilization is obtained by means of an explicit form of exponential energy decay estimate.

Metal Uptake by Plants from Soil Contaminated by Thin-Film Solar Panel Material

Various metals, including zinc (Zn), nickel (Ni), aluminum (Al), chromium (Cr), gallium (Ga), lead (Pb), copper (Cu) and indium (In), may be released and cause contamination when scrapped end-of-life (EoL) Cu(InGa)Se2 thin-film solar panel (CIGS TFSP) is buried in the soil. In this study, we grew Brassica parachinensis L. H. Bariley (VegBrassica) in three different types of soils, namely, a commercial soil, a Mollisol, and an Oxisol, which had been contaminated by CIGS TFSP to various extents. The concentrations of contaminants in these soils were positively correlated with both the amount of CIGS TFSP added and the burial period. Plants grew well in commercial soil and Mollisol, but those in Oxisol showed prominent signs of chlorosis and died after 30 days. The bioaccumulation factor (BF) and concentration of Zn in VegBrassica grown in commercial soil with 10% of CIGS TFSP added were 3.61 and 296 mg/kg, respectively, while the BF and concentration of In of VegBrassica grown in Mollisol were 3.80 and 13.72 mg/kg, respectively. The results showed that soils were contaminated by metals released from CIGS TFSP, and different adsorption patterns were observed for VegBrassica?depending on which types of metals associated with the soil properties.

The Disagreement between Anisotropic-Isotropic Diffuse Solar Radiation Models as a Function of Solar Declination: Computing the Optimum Tilt Angle of Solar Panels in the Area of Southern-Italy

In this paper a simulation to maximize the global solar radiation on a sloped collecting surface was applied to typical latitudes in the area of southern Italy, to calculate the optimum tilt angle of solar panels on building structures or large photovoltaic power plants located in that geographical area. Indeed, the area of southern Italy and in particular Sicily and Calabria are the top of European locations for acquiring solar energy. Some models of diffuse solar irradiance were taken into account to determine panels inclinations that maximized the impinging solar radiation by means of global horizontal solar radiation data provided from the Italian Institute of ENEA (Italy). An algorithm was used for the simulation providing a set of tilt angles for each latitude. The optimum tilt angle values obtained from the simulation resulted to be strictly related to the model of diffuse solar radiation that was used. Indeed, the disagreement between the values obtained using anisotropic models of diffuse solar radiation and those obtained from the isotropic model resulted to decrease significantly with increasing solar declination, showing that the isotropic model can be reliable only in summer months.

Thin Film Photovoltaic/Thermal Solar Panels

A solar panel is described,in which thin films of semiconductor are deposited onto a metal substrate.The semiconductor-metal combination forms a thin film photovoltaic cell,and also acts as a reflector-absorber tandem,which acts as a solar selective surface,thus enhancing the solar thermal performance of the collector plate.The use of thin films reduces the distance heat is required to flow from the absorbing surface to the metal plate and heat exchange conduits.Computer modelling demonstrated that,by suitable choice of materials,photovoltaic efficiency can be maintained,with thermal performance slightly reduced,compared to that for thermal-only panels.By grading the absorber layer-to reduce the band gap in the lower region-the thermal performance can be improved,approaching that for a thermal-only solar panel.

Smart Monitoring of Solar Photovoltaic Panels by the Approach of Machine Learning

The exploitation of renewable energy has become a pressing task due to climate change and the recent energy crisis caused by regional conflicts.This has further accelerated the rapid development of the global photovoltaic(PV)market,thereby making the management and maintenance of solar photovoltaic(SPV)panels a new area of business as neglecting it may lead to significant financial losses and failure to combat climate change and the energy crisis.SPV panels face many risks that may degrade their power generation performance,damage their structures,or even cause the complete loss of their power generation capacity during their long service life.It is hoped that these problems can be identified and resolved as soon as possible.However,this is a challenging task as a solar power plant(SPP)may contain hundreds even thousands of SPV panels.To provide a potential solution for this issue,a smart drone-based SPV panel condition monitoring(CM)technique has been studied in this paper.In the study,the U-Net neural network(UNNN),which is ideal for undertaking image segmentation tasks and good at handling small sample size problem,is adopted to automatically create mask images from the collected true color thermal infrared images.The support vector machine(SVM),which performs very well in highdimensional feature spaces and is therefore good at image recognition,is employed to classifying the mask images generated by the UNNN.The research result has shown that with the aid of the UNNN and SVM,the thermal infrared images that are remotely collected by drones from SPPs can be automatically and effectively processed,analyzed,and classified with reasonable accuracy(over 80%).Particularly,the mask images produced by the trained UNNN,which contain less interference items than true color thermal infrared images,significantly benefit the assessing accuracy of the health state of SPV panels.It is anticipated that the technical approach presented in this paper will serve as an inspiration for the exploration of more advanced and dependable smart asset management techniques within the solar power industry.

Great wall of solar panels to mitigate yellow dust storm

Mitigation of the large scale yellow dust storm is a serious problem facing China. We propose the approach of building windbreak walls equipped with solar panels in the proximity of dust origins. The solar panels generate electricity in the sunny days; the walls break the wind and remove airborne dusts based on the impactor principle during wind storms. Preliminary calculation indicates the walls may be able to remove the major fraction of the airborne dusts and the generated electricity could be significant. More detailed studies are needed to prove the feasibility of the approach.

Utilization of Solar Energy in Irrigation Systems in Bangladesh

The demand for water pumping in urban water supply and irrigation in Bangladesh is significantly influenced by electricity deficits and high diesel costs. To address these challenges, the adoption of solar power for water pumping emerges as a viable alternative to traditional systems reliant on grid power and diesel. In recent years, there has been a growing emphasis on clean and renewable energies, aligning with the environmental and economic priorities of Bangladesh. The agricultural sector, serving as the backbone of the country’s economy, witnesses an escalating demand for water as the population increases. The extraction and transfer of water for agricultural and drinking purposes translate to high-energy consumption. Leveraging the abundant and essentially free solar energy, particularly during the crop growth periods when irrigation is crucial, presents an optimal solution. This study underscores the underutilization of this vital resource in Bangladesh and advocates for the widespread implementation of solar energy conversion programs, specifically in photovoltaic pumping systems. By comparing these systems with conventional diesel pumps, this paper aims to inspire policymakers, statesmen, and industry professionals to integrate green energy into the water sector. The envisioned outcome is a strategic shift towards sustainable development, with a focus on harnessing solar power to pump water for villages and agriculture, thus contributing to economic and environmental sustainability.

Tilt Angle Optimality Criteria for Stand Alone PV Systems

The conventional approach to optimizing tilt angles for fixed solar panels aims to maximize energy generation over the entire year. However, in the context of a supply controlled electric grid, where solar energy availability varies, this criterion may not be optimal. This study explores two alternative optimization criteria focused on maximizing baseload supply potential and minimizing required storage capacity to address seasonality in energy generation. The optimal tilt angles determined for these criteria differed significantly from the standard approach. This research highlights additional factors crucial for designing solar power systems beyond gross energy generation, essential for the global transition towards a fully renewable energy-based electric grid in the future.

基于孪生网络的自监督太阳能电池板裂纹检测方法

太阳能电池板的裂纹缺陷检测能够避免电能转换效率低,以及短路造成起火的损失。针对现存对比学习方法中存在细微裂纹漏检导致检测精度低,并且严重依赖构建负样本等问题,提出了一种基于孪生网络的两阶段自监督裂纹检测方法。第1阶段提出了一种基于卷积神经网络(convolutional neural network,CNN)和Transformer的预训练编码器模型,通过孪生网络架构学习样本的精细特征表示,提高对电池板细微裂纹的特征表示能力;第2阶段基于预训练模型在少量标注样本下学习分类器以区分缺陷样本。为进一步区分不影响电池板功能的纵向裂纹,另增加了一个分类头进行判别。在ELPV数据集上的实验结果表明,方法在测试准确度方面优于其他相关检测方法,在只对数据进行少量标注的情况下准确度达到83.26%,单张检测时间为6.1 ms,同时在裂纹图像中检出纵向裂纹的召回率也有76.7%。

Modelling of Solar Power Production in Dry and Rainy Seasons Using Some Selected Meteorological Parameters

In this paper, we deployed the multiple linear regression method in developing a solar power output model for solar energy production, where the meteorological parameters are the independent variables. We fitted the model and found that the meteorological variables considered accounted for 94.88% and 99.61% of the power output in both dry and rainy seasons. We observed from the work that the solar panel performs well in all seasons but slightly better in the rainy seasons. This could be attributed to the washing away of dust particles from solar panels by the rain and higher operating temperature different from the specified manufactured temperature of 25°C. We observed that other factors such as the cloud slightly affect the optimal performance of the system. Panels inclined at an angle of 5° (Tilt) and facing south azimuth performs optimally, periodic washing of the surface of solar panels enhances optimal performance.