Solar Panels Research Articles

A novel approach for identification of PV cell model parameters with Grey Wolf Election-Based Optimization algorithm

In the past few years, photovoltaic production has significantly increased worldwide and has become a necessary element for achieving global agreements to minimize carbon dioxide emissions. Therefore, a precise and reliable simulation of PV panels is of increasing interest to researchers. However, this task is predicated on determining the PV model parameters; thus, it continues to be a highly challenging task. To overcome this challenge, we propose a swarm-based human optimization algorithm, i.e. the Grey Wolf Election-Based Optimization algorithm. This new approach combines inventive human behavior in the election process with the powerful search ability of grey wolves. The root-mean-square deviations predicted by the Grey Wolf Election-Based Optimization algorithm for a RTC France cell are 7.7386E-04, 7.5644E-04, and 7.4965E-04 for single, double, and triple-diode models, respectively. A total of thirteen of the most cited and newest meta-heuristic algorithms were employed as competitors to adequately assess the performance of the proposed algorithm. The experimental results demonstrate that the proposed algorithm outperformed the compared algorithms and provided more efficient solutions.

Assessing Urban Morphology's Impact on Solar Potential of High-Rise Facades in Hong Kong Using Machine Learning: An Application for FIPV Optimization

The relationship between solar potential on building façade and urban morphology at urban scale remains unclear, and the design of façade integrated photovoltaic (FIPV) lacks evidence. This study investigates high-rise, high-density commercial districts in Hong Kong (HK), using Random Forest algorithm combined with the SHapley Additive exPlanations method to assess the importance of urban morphology on solar irradiance on building facades. The results indicate that plot ratio, building floor, building density, and perimeter shape factor are the four key parameters influencing solar irradiance, with the importance rate and contribution value of the four parameters reach 43.9% and 48.7%, respectively. Based on these parameters and actual urban blocks in HK, typical urban typologies were constructed. Four scenarios were generated with plot ratio as the control parameter. The positions, amounts, and transparency of PV glass on the south and east facades were optimized to minimize the payback period and maximize power generation, using Non-dominated Sorting Genetic Algorithm II algorithm. The south façade of Scenario 1 (when the heights of surrounding buildings are lower than that of the targeted building) obtained the optimal payback period (8.44 years) and power generation (55961 kWh), with 77 opaque PV panels and 49 semi-transparent ones.

Comprehensive evaluation of solar floating photovoltaic prospective in Saudi Arabia: Comparative experimental investigation and thermal performance analysis

Recently, floating photovoltaic systems have been regarded as a promising technology for producing clean energy by utilizing the surface of water bodies, such as lakes, rivers and oceans. This study introduces a comparative experimental study and energy performance evaluation of a 1.0 kW offshore floating photovoltaic (FPV) system and a nearby traditional ground-based PV system (GPV) installed in the eastern province of Saudi Arabia. The FPV system was deployed in the Arabian Gulf, 25 m off the coast, at an average depth of 1 to 1.5 m depending on tide, wave, and current intensity. The FPV system employs a strong novel eco-friendly platform structure made of recycled buoyant materials such as engineered plastic drums and wood. This system is anchored with tension cables and concrete blocks that can withstand the changing sea water conditions. The GPV system, on the other hand, was installed 150 m inland from the shore. Real-time monthly data monitoring and assessment indicated that FPV systems outperformed GPV systems in terms of lower PV panel surface temperatures, higher power output, and panel efficiency. Based on daily average back surface temperatures, FPV system temperature was decreased by 7.5 % to 21.34 % when compared to GPV. Moreover, the FPV system efficiency was also increased by 12.2 % when compared to the GPV system. This study aims to assist in promoting the applicability of solar floating photovoltaic systems to synergistically fulfill the requirements of sustainable electricity production for the arid costal community in Saudi Arabia and similar areas.

Innovative materials integrated with PCM for enhancing photovoltaic panel efficiency: An experimental investigation

The electrical power generated by a photovoltaic panel is crucial due to the high demand for electricity. Operating temperatures above the Standard Test Conditions (STC) negatively impact the output power, which is a significant drawback of this technology. Therefore, it is essential to keep the front and back surface temperatures of the photovoltaic panel as low as possible. Phase Change Material (PCM) is a good passive cooling method, but it has low thermal conductivity. This paper introduces a novel material to address this issue. Iron Filling Waste (IFW) is integrated with PCM to cool the photovoltaic panel. This investigation is presented experimentally with three modules. The highest surface temperature of the reference panel, 78 °C, is recorded for the uncooled module, while with PCM- IFW is 70.5 °C. The comparative analysis shows that the PV module cooled by PCM-IFW achieved an efficiency enhancement and power output increase of 39 % and 33 % respectively, compared to the reference panel without cooling. IFW-PCM achieves an average enhancement in efficiency and output power by 23 % and 11 %, respectively, compared with panels that use PCM only. Also, IFW enhances thermal conductivity without any additional economic cost for the cooling systems.

Wind-induced response and control criterion of the double-layer cable support photovoltaic module system

The cable support photovoltaic module system has obvious characteristics of wind-induced vibration. In order to study the wind-induced vibration response characteristics and mechanism of the double-cable support photovoltaic module systems, and further discuss the stiffness control criterion. The wind-induced vibration response of a new type of cable-truss support photovoltaic module system with a span of 35m is studied through the aeroelastic wind tunnel test. Firstly, the scaled aeroelastic test model was established to meet the aeroelastic test requirements. Then, the effects of wind direction, PV module inclination angle, and stability cable initial prestress on the wind-induced vibration response characteristics under uniform flow and turbulent field are studied. Finally, the wind-induced vibration response mechanism and stiffness control criterion are discussed. The results show that the increase of inclination angle will lead to a decrease in critical wind speed, the 0° wind direction is the most unfavorable, and the increase of initial prestress can increase the critical wind speed but is inefficient. The critical wind speed under the turbulent flow field is about 30% higher than that of the uniform flow field. The instability vibration is the result of multi-mode coupled vibration of vertical bending and torsion. It is suggested that the stiffness control criterion is more appropriate as 1/100. The research results are of great significance for the design and application of the cable support photovoltaic module system.

Metode Simple Additive Weighting (SAW) pada Pemilihan Jenis Lampu Penerang Jalan Berbasis Photovoltaic

The simple additive weighting method is a decision support system method for various decision selection problems. Many research topics use the SAW method as a reference method in a decision support system. The street lighting control system is very useful for residents of the village of Karanganyar, Paiton, Probolinggo. This lighting is the main spotlight for residents' activities at the village office. Such as August competitions, routine monthly recitations, competitions to commemorate Isra' Mi'raj and the Prophet's birthday. There are many types of lamps in stores. The criteria for lamps are that they have high light illumination, long-lasting light, large bulb size, so that they can help village residents carry out activities at night. This lamp also uses photovoltaic as an additional source of electrical energy so it can save electricity costs to PLN. The solar panel measures 550 x 360 x 25 mm, 20 volts and has a peak power of 20 watts

Sustainable management of end of life crystalline silicon solar panels in Australia: Advancing circular economy practices

The worldwide adaptation of Photovoltaic (PV) technology as a sustainable alternative to fossil fuels, has experienced exponential growth in recent years. However, the lack of effective waste management policies has hindered efficient PV panel disposal and recycling practices. In the absence of effective policies, the worldwide End-of-Life (EoL) PV module accumulation is predicted to reach a critical stage in the early 2030s. This study examines the environmental impacts of different EoL management practices using Life Cycle Assessment (LCA), based on a comprehensive database generated from both literature and industrial data. Five different EoL scenarios were considered for 1000 kg of Crystalline Silicon (c-Si) PV modules with a focus on Australia as a case study, while considering the energy recovery options and emphasizing the economic benefits. From a comprehensive LCA study, it is found that upcycling options reduce the environmental impacts significantly compared to downcycling, while chemical treatment emerges as a preferred option, demonstrating a reduced environmental burden. Nevertheless, the utilization of toxic chemicals during chemical treatment-based PV recycling needs to be reconsidered. Additionally, the usage of chemicals during the metal recovery process of PV module recycling caused the highest environmental burden, necessitating the importance of moving to greener treatment practices. This research study will enable the identification of optimum EoL c-Si module recycling options, contributing to sustainable waste management.

An innovative Solar-Power fed atmospheric water Generator: Quantity and quality Assessments

This paper presents the generation of an alternative source of clean water through the Atmospheric Water Generation technique. A customised design using a thermoelectric cooler, powered by a renewable energy source, was used for this purpose. The system is powered by a 65-watt solar panel coupled with piezoelectricity generation utilising the mechanical power of pedestrians. As piezoelectricity production was diminutive, it was only used to supply energy to a low-power appliance. Several customized designs, as well as systematic orientations of the prototype, were examined under varying temperatures and humidity to optimise water production. It is found that the best system configuration can produce about 100 ml of water after 6 h of operation at 66 % average relative humidity and an ambient temperature of 31 °C. The water generation was a complete off-grid solution using solely solar power. As per the accumulated water-to-power input ratio, the proposed system has the maximum water generation capability (0.8333 ml h − 1 W − 1) amongst the contemporary systems. The drinkability of accumulated water samples was tested considering the Heterotrophic Bacterial and total Coliform counts, accompanied by a comparison between tap water and distilled water.

A Stochastic Sustainable Closed-Loop Supply Chain Networks for Used Solar Photovoltaic Systems: Meta-Heuristic Comparison and Real Case Study

This research presents a novel approach to setting up a sustainable Closed-Loop Supply Chain (CLSC) network for used solar photovoltaic (PV) systems, addressing end-of-life product waste from solar panel installations and manufacturing centers. The model accounts for uncertainties in PV systems and aims to efficiently collect, refurbish, and recycle used solar PV systems, promoting a circular and environmentally responsible waste management strategy. The supply chain network comprises vendors, collection centers, hybrid centers, distribution centers, and manufacturing centers, with objectives to maximize total profit, minimize environmental risk, and maximize service levels by demonstrating the profitable reuse of used solar photovoltaic systems by manufacturers. The epsilon-constraint method is utilized to handle the model's multi-objectiveness and identify Pareto optimal solutions. A case study in Iran is conducted to validate the methodology's performance, comparing results obtained from three meta-heuristic methods: Non-Dominated Sorting Genetic Algorithm-II (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Gray Wolf Optimization (MOGWO). The average error rates are 0.0358 for MOGWO, 0.1248 for MOPSO, and 0.2066 for NSGA-II. Sensitivity analysis highlights the significant impact of demand variations on all objective functions. Lastly, the numerical results are discussed to provide managerial insights for informed decision-making.

Optimal scheduling of smart home appliances with a stochastic power outage: a two-stage stochastic programming approach

This study primarily concentrates on enhancing the scheduling of electric appliances within a smart home equipped with a photovoltaic solar array and a storage battery capable of redistributing excess electricity to the grid. Furthermore, the investigation takes into consideration the unpredictability of a power outage, analyzing the scheduling of these appliances to minimize consumer electricity expenses. To tackle this issue, a two-stage stochastic programming methodology is developed to effectively model the uncertainty surrounding both the onset time and duration of power outages. Additionally, a sample average approximation algorithm (SAA) is devised to efficiently address the problem. Through extensive computational experiments, the outcomes demonstrate that the SAA yields shorter CPU processing times, albeit without guaranteeing optimal solutions. The total average percent deviation of the SAA's upper bound from its lower bound and the optimal solution stands at 4.16% and 4.5%, respectively. Moreover, it is demonstrated that utilizing the stochastic approach, as opposed to the deterministic one, can enhance solution quality by 6.2%. Furthermore, a comprehensive sensitivity analysis is provided, focusing on probability distribution functions of outage start time and duration, alongside an analysis of the solar panel and battery storage capacities. It is revealed that when adopting a Normal distribution instead of a Uniform distribution, the performance of the SAA experiences a slight decline.

Progress in Zero-gravity Unloading Test and System Research of Solar Wing

Aim: The solar wing undertakes the important task of providing energy for spacecraft. The deployment test of the solar wing in a zero gravity environment using a zero gravity system on the ground is an important link to ensure the safe and stable deployment of the solar wing in space. This article briefly describes the development process and research progress of solar wings, introduces the ground zero gravity test methods and test platforms for solar wings, including some patents for improving the ground zero gravity test platform for solar wings, and points out some urgent problems that need to be solved in the zero gravity unloading test and system at present. Background: The solar wing needs to be deployed in the weightlessness environment of space before working. Objective: The objective of this study is to summarize the zero gravity experimental methods and systems of solar wings, introduce their categories, characteristics, and development. Methods: This article summarizes various scientific research achievements in the zero gravity test of solar wings, and introduces the advantages and disadvantages of the zero gravity test method of solar wings. Conclusion: Although research on zero-gravity unloading test systems for solar wings is continuously advancing, there are still several technological bottlenecks that need to be overcome in terms of system precision, payload capacity, dynamic simulation, method optimization, diversifying zero-gravity test methods and system universality for different types of solar panel unloading.

Electric Bicycle Battery Charging System Design Using Solar Panel

This solar-powered electric bicycle charging system design and development uses solar panels as a renewable energy source to charge the battery, which drives the electric motor. The system consists of four main components: solar panel, solar charge controller, battery, and electric motor. The solar panels are connected in parallel to generate 36V, which is then stored in the battery. The system is designed to optimize energy harvesting and reduce dependence on fossil fuels. Testing was conducted with research stages, including system design, hardware implementation, and testing. The results show that the system can charge the battery efficiently, with a charging time of 3 hours. The system was also tested under different conditions, including morning, afternoon, and night, to evaluate its performance. It can be concluded that the solar-powered electric bicycle charging system is a viable alternative to traditional fossil fuel-based systems. The system is environmentally friendly, reduces energy dependence, and provides a sustainable solution for transportation. The results of this research can be used to develop a more efficient and sustainable transportation system.

Automation System of Water Circulation and Fish Feeding in Tilapia Fish Pond with Solar Panel Source

Tilapia cultivation requires good management of water quality and proper feeding to ensure optimal fish growth. One of the main challenges in this process is maintaining the water pH level within ideal limits and providing feed according to the needs of the fish based on their size. In addition, limited access to electricity in some locations makes fish pond management less efficient. This study aims to design an automated water circulation system that is regulated based on the water pH level and automatic feeding according to the needs of the fish, by utilizing solar panels as a power source. This system uses a pH sensor to monitor water conditions in real time and regulate water circulation to maintain ideal pH levels. Feeding is regulated based on the size and number of fish to ensure optimal feed efficiency and fish growth. This system is powered by solar panel energy, so it can operate independently without relying on conventional electricity networks. Test results show that the system is able to maintain water pH stability and automate feeding with high accuracy, while utilizing renewable energy effectively. The use of solar panels not only reduces dependence on electricity, but also reduces operational costs and supports environmental sustainability. This system is expected to increase the efficiency and productivity of tilapia cultivation, especially in areas with limited access to electricity.

Design analysis of a sustainable techno-economic hybrid renewable energy system: Application of solar and wind in Sigulu Island, Uganda

Sustainable energy is central to achieving the global Sustainable Development Goals (SDGs), particularly in electrifying underserved communities. This study examines Sigulu Island, which lacks grid electricity and relies on costly, polluting diesel generators. On-site assessments revealed a daily load demand of 1,455.705 kWh. This study designed and analyzed a Sustainable Techno-economic Hybrid Renewable Energy System (STHRES) combining solar photovoltaics and wind turbines, with battery backup, to meet the island's energy needs. The research adopted both qualitative and quantitative methods, gathering atmospheric weather conditions specific to Sigulu Island. Solar panels and wind turbines were identified as the most viable options, with the system incorporating 677 units of 1 kW solar panels and 27 units of 1 kW wind turbines, generating 839.97 kW and 640.08 kW daily, respectively. Additionally, 527 Li-ion batteries were used to store approximately 1480.05 kW of surplus energy demand to manage fluctuations capable of sustaining the island for 8 hours during a total blackout. The initial installation costs are estimated at $90,393.04 for solar PV, $27,729.82 for wind turbines, $159,169.81 for batteries, and $92,407.00 for the inverter. The STHRES is projected to save $56,917.93 annually, covering 15.4 % of the installation costs compared to diesel operations. Moreover, this system will reduce Uganda's carbon footprint by 436,035.6 kgCO₂ annually, equivalent to a 0.01 % reduction in national emissions. The proposed system decreases the Net Present Cost (NPC) from $426,617.60 to $369,699.67 and the Cost of Energy (COE) from $32.12/kWh to $ 27.79/kWh. With a 9 % Internal Rate of Return (IRR) and a 3 % Return on Investment (ROI), STHRES has a payback period of 8.2 years, demonstrating its financial and environmental benefits for Sigulu Island.

Cost-Effective Dust Detection on Solar PV Panels through Deep Learning: A Step towards Automated Maintenance Systems

Abstract: Accumulation of dust on solar panels impacts the overall efficiency and the amount of energy it produces. Detecting and mitigating dust accumulation is therefore crucial for optimizing solar energy production. While various techniques exist for detecting dust to schedule cleaning, many of these methods uses licensed software like MATLAB, which can be financially burdensome. This study proposes the use of modified pre-trained ResNet-50 model architecture with an adjusted fully connected layer for binary classification. An experimental setup was installed utilizing a single 75 Wp panel with an inclination maintained at 30-degree angle. The fine dirt particles were artificially introduced and datasets of images of clean and dusty panels were collected from five different sides were taken, to compensate for the surface reflectance from the PV panel due to camera angles. Those datasets were used to train and test the model and the accuracy achieved was 90%. The model's ability to detect dust with minimal false positives ensures more efficient maintenance scheduling. This research demonstrates the potential of AI-driven dust detection systems to enhance the operational efficiency of solar PV installations.

PENERAPAN ENERGI TERBARUKAN DI KAWASAN WISATA LUMBUNG MATARAMAN DESA BENDUNG GUNUNGKIDUL

The community service activities carried out focus on the application of renewable energy in the Lumbung Mataraman tourism area, Bendung Village, Semin, Gunungkidul. The community service activities carried out are strategic steps in supporting a sustainable green economy. By utilising appropriate technology (TTG) in the form of solar panels and wind turbines, this area is expected to reduce dependence on fossil energy and increase operational energy efficiency. In addition, the tourism area has the potential to become an educational model on the use of renewable energy, both for local communities and tourists. The methods used in this research include qualitative and quantitative approaches. The qualitative approach was conducted through interviews with local stakeholders, such as tourism area managers, communities, and renewable energy experts. In addition, field observations were conducted to understand energy needs and the potential for implementing renewable energy technologies. The quantitative approach involved analysing current energy consumption data and calculating the potential of renewable energy that can be applied, such as solar panels and wind turbines. The results of the activities show that the implementation of renewable energy in Lumbung Mataraman can reduce dependence on fossil energy sources, lower carbon emissions, and provide additional economic benefits through energy cost savings and educational media in the use of renewable energy.

Prospects of electricity generation through the use of alternative sources such as waste tires processing products

The object of this study is a hybrid energy system that integrates the processes of recycling used tires with the use of alternative energy sources to design a sustainable and environmentally safe power generation system. The problem addressed in this study is to find effective methods for recycling waste tires for electricity generation, as traditional recycling methods are energy-intensive and lead to the loss of valuable resources. Three methods of tire processing have been considered: pyrolysis, thermal degradation, and mechanical grinding. The method of electricity generation using pyrolysis has demonstrated high environmental performance due to a significant reduction in greenhouse gas emissions and a high rate of renewable energy (80 %). According to the results of the study, the use of 5 MW pyrolysis furnaces in combination with 500 kW solar panels provides a reduction in dependence on fossil sources, reducing CO2 emissions to 50.0 kg/year. The method has high capital costs (NPC USD 4.2 million), but the cost of energy production (COE USD 0.18/kWh) makes this method competitive in the long run. Thermal degradation provides a balanced approach in terms of energy efficiency (75 %) and environmental performance. Although its CO2 emissions are higher than in pyrolysis, the method makes it possible to obtain additional products that could be used in other industries, thereby increasing economic efficiency. Mechanical shredding has the lowest average energy costs (USD 0.12/kWh) and the lowest CO2 emissions (30.0 kg/year), making it ideal for cost-conscious businesses. The study confirms the possibility of effective integration of renewable sources with tire recycling methods. The practical use of the research results is possible during the implementation of grant projects for the recycling of used tires, and in the work of relevant government structures for devising a policy for the disposal of used tires

Deep Learning for Solar Panels Defect Classification Using Data Augmentation Strategies

The inspection and maintenance of solar panels face significant challenges due to the dangers involved in checking for potential defects in photovoltaic panels. However, the advancement of studies to facilitate this verification is limited by the lack of image datasets for training algorithms that can perform this task. Based on this principle, the use of Convolutional Neural Networks (CNNs) for training and Data Augmentation for creating artificial data from real images is common. With that said, this work aims to explore configurations and models of Data Augmentation for the classification of defects in solar panels using CNNs. The proposed methodology consists of four experimental stages, where the application of Zoom, rotation, horizontal displacement, and vertical displacement transformations are evaluated, followed by filtering the best results and combining them to find the ideal classification model. The performance of the model proved to be most effective when using a 35-degree rotation for creating artificial images, thus achieving an 88% F1-Score and 87.64% accuracy.

Numerical Investigation on Photovoltaic Thermal Panel Using Various Nanofluids Concentrations

Increasing the efficiency of solar panels is crucial for effective use of renewables. The present numerical study deals with improving the performance of a PVT system with nanofluid using CFD FLUENT software. ZnO-water and SiO2-water nanofluids are investigated and correlation are established between the PVT efficiency and various nanofluid volumetric concentrations ranging from 1% to 10%. Validation of the present results is verified by comparison with experimental data. Comprehensive research is conducted to evaluate the correlation between the thermophysical properties of nanofluids such as density, thermal conductivity, specific heat capacity and dynamic viscosity. The results demonstrate that the overall efficiency of the ZnO-water nanofluid and -water nanofluid increases by 0.44% and 0.24%, respectively, as the volumetric concentration of the nanofluid rises from 1% to 10%. The ZnO-water nanofluid reveals enhanced thermal and electrical efficiency compared to the -water nanofluid due to its superior thermal conductivity and enhanced heat transfer capabilities along the absorber tube. The ZnO-water nanofluid exhibits a greater heat transfer coefficient, thereby facilitating the cooling mechanism of the PV panel and reducing the PV cell temperature, hence enhancing power generation.

Solar Wireless Electric Vehicle Charging System

This project outlines the design and implementation of a solar-powered electric vehicle charging station that addresses the dual challenges of high gasoline prices and harmful emissions. The number of countries adopting electric vehicles continues to rise, as they not only promote environmental sustainability but also significantly lower transportation costs by substituting expensive fuels with more affordable electricity. In this context, developing an electric vehicle charging infrastructure offers an innovative and effective solution. This system enables electric vehicles to charge while in motion, eliminating the need for stationary charging stops. Powered entirely by solar energy, it requires no additional power supply. The system incorporates solar panels, batteries, transformers, regulator circuits, copper coils, AC/DC converters, Atmega328P controllers, and LCD displays. This technology demonstrates the feasibility of an on-road solar-powered wireless charging system for electric vehicles.