Solar Panels Research Articles

Preparation and study of polyvinylidene fluoride film with high UV aging resistance

Abstract Polyvinylidene fluoride materials were widely used in the preparation of solar panel films due to their excellent weather resistance and heat resistance. In this work, Polyvinylidene fluoride films with UV absorbers (PF/UV films) were prepared by customized extrusion equipment. The properties of these films were demonstrated with a scanning electron microscope (SEM), water contact angle (WCA), thermogravimetric analyzer (TGA), differential scanning calorimetry (DSC), and tensile strength tester. Furthermore, the SEM observation suggested that PF/UV films had smooth surfaces without significant defects. The WCA results illustrated that PF/UV films had better hydrophobicity than polyvinylidene fluoride films. The TGA and DSC curves indicated that the initial decomposition temperature and the melting temperature of the PF/UV film were 365.1°C and 169.2°C, respectively. Furthermore, the results of UV transmittance indicated that the UV aging resistances of PF/UV film were higher than that of polyvinylidene fluoride film.

Effect of Thermal Environment on Heat Transfer Mechanism of Solar-Powered Balloon

Solar energy units, as an ideal alternative, can be supplied to the balloons by mounting them over the envelope of the balloon. To avoid superheating and superpressure, the solar-powered balloon’s thermal efficiency should be controlled. In the current research, the thermal model of a solar-powered zero-pressure balloon has been built up and simulated in FORTRAN via iterative techniques and validated with published experimental data. The simulation of stratospheric solar-powered balloon flight has been carried out in real seasonal atmospheric conditions. The temperature variation of solar panels, interior helium, and balloon envelope material has been observed and compared with the unpowered balloon in winter and summer atmospheric conditions. The output power performance of the photovoltaic panel placed over the balloon with and without thermal effect has been reported. Furthermore, the characteristic length, angle, and area of the solar panel effects on photovoltaic panel temperature and output power are discussed in detail. The maximum temperature of the helium and envelope of the solar-powered balloon is obtained as 350.03 K and 351.19 K, respectively, and is notable in the summer solstice compared with the unpowered balloon. This research would help design the energy unit of the stratospheric balloon for long endurance.

Solar irradiation intensity forecasting for solar panel power output analyze

Solar irradiation intensity forecasting for solar panel power output analyze

Earth shadow time/accelerometer/GNSS-based geosynchronous transfer orbit determination method for electric propulsion satellite

Autonomous orbital maneuvering of an electric propulsion satellite in geosynchronous transfer orbit (GTO) requires more accurate autonomous orbit determination (OD). Implementing the traditional autonomous OD method for the electric propulsion satellites may result in the accumulation of measurement errors and dynamic errors from orbit extrapolation, which significantly restrict the OD accuracy. Fortunately, an electric propulsion satellite in GTO can sense the transit time in or out of the Earth shadow according to the output power change of the solar array. Therefore, an Earth shadow time/accelerometer/GNSS-based geosynchronous transfer OD method for electric propulsion satellites is proposed in this paper. First, a system state model with dynamics is established for the entire orbit transfer process. Second, measurement models that use the measurement information from the accelerometer, GNSS, and the transit time into or out of the Earth shadow are established. Third, a sequential filtering estimation approach is designed to solve the problem of asynchronous data fusion for autonomous OD. Comparison simulations demonstrate the effectiveness and superiority of the proposed method.

Lead Iodide Redistribution Enables In Situ Passivation for Blading Inverted Perovskite Solar Cells with 24.5% Efficiency.

Blade-coating stands out as an alternative for fabricating scalable perovskite solar cells. However, it demands special control of the precursor composition regarding nucleation and crystallization and currently exhibits lower performance than the spin-coating process. It is mainly the resulting film morphology and excess lead iodide (PbI2) distribution that influences the optoelectronic properties. Here, the effectiveness of introducing N-Methyl-2-pyrrolidone (NMP) to regulate the structure of the perovskite layer and the redistribution of PbI2 is found. The introduction of NMP leads to the accumulation of excess PbI2, mainly on the top surface, reducing residual PbI2 at the perovskite buried interface. This not only facilitates the passivation of perovskite grain boundaries but also eliminates the potential degradation of the PbI2 triggered by light illumination in the perovskite buried interface. The optimized NMP-modified inverted perovskite solar cell achieves a champion efficiency of 24.5%, among the highest reported blade-coated perovskite solar cells. Furthermore, 13.68cm2 blading perovskite solar modules are fabricated and demonstrate an efficiency of up to 20.4%. These findings underscore that with proper modulation of precursor composition, blade-coating can be a feasible and superior alternative for manufacturing high-quality perovskite films, paving the way for their large-scale applications in photovoltaic technology.

Vapor-phase fluoride treatment: A stability breakthrough for perovskite solar modules

Vapor-phase fluoride treatment: A stability breakthrough for perovskite solar modules

OPTIMAL DISTRIBUTION OF ADDITIONAL ELECTRICAL POWER

The problem of optimal distribution of additional electric power for private houses and households with solar panels or stations in adverse weather conditions over a long period of time is considered. This problem is very relevant for regions where the supply of electric power to houses and households is carried out mainly by green technology. Based on this, it is shown that when weather conditions worsen, which can last for a long time, a problem arises of providing these objects with energy allocated from backup power lines, the capacity of which is insufficient for full energy supply. To solve this problem, especially in the case of a shortage of allocated power, when it is not enough to satisfy all consumers to the maximum, the criteria for distributing the allocated limited power among houses are analyzed, examples are given for calculating the residual energy of a deep-discharge battery for each household in the event of prolonged bad weather conditions. Two options for solving the problem of optimal distribution of the allocated power are considered, taking into account the full simulation model of houses and households with electricity supply from solar panels: - distribution by residual energy taking into account the energy required to charge the batteries of private houses; - distribution by residual energy without charging the batteries. The solution to the problem of distributing limited energy between houses with solar panels is shown using the example of 5 households. For this purpose, the initial conditions and restrictions for each house are modeled. The problem of optimal distribution of allocated energy between these objects is formulated. The model of the problem itself is built and solved using the SOLVER subroutine of the Excel program. The main tables are given, in which it is necessary to enter restrictions, optimization criteria and the objective function, screenshots of the solution of this problem in two versions with the introduction of all restrictions in separate Excel windows. It is shown that solution of the issue of optimal distribution of additional energy allocated to eliminate the shortage of energy in conditions of limitation allows to the following opportunities: - provision of additional energy depending on the time of complete depletion of energy in the house prevents overloading of the electric network, incorrect openings in power lines and other unpleasant situations; - the inclusion of the module of optimal distribution of additional energy into the model during simulation, allows to save time for the seeking of additional energy and thus to increase the overall efficiency of the system. Keywords: solar panels, bad weather, additional energy, optimal distribution.

Using AMAZON REKOGNITION artificial intelligence to recognize bank branch solar panels.

Bank branches carry out quality control of solar panels that ensure energy independence and uninterrupted operation of computer equipment in the conditions of an aggressive war caused by Russia, to ensure that the solar panels meet production standards and avoid potential damage to the brand reputation. As sensor costs decrease and connectivity increases, industry is using real-time image analysis to detect quality issues. At the same time, advances in artificial intelligence (AI) enable advanced automation, reduce overall project cost and time, and provide accurate defect detection results in manufacturing plants. As these technologies evolve, AI-assisted inspections are becoming more common outside of the factory environment.

High gain super lift Luo converter for hybrid energy management systems

Recently, there has been a resurgence of interest in the development of alternative energy supplies due to rising fossil fuel prices and issues about the effects of greenhouse gas emissions on the environment. Today's demand is the design of a hybrid electric power generation system that uses solar and wind energy for remote places. This paper presents the design of an optimized hybrid renewable energy system consisting of a photo voltaic wind turbine (savinous type) with a battery and a super lift LUO converter. A solar PV module with an intelligent inverter facilitates the tracking of the maximum power point and the implementation of a battery charge controller for greater battery lifespan as well as reliable performance. Higher power output and clean energy are available from the hybrid power generation system, which combines innovative techniques with solar and wind power.

Deep Learning-Driven Thermal Imaging Hotspot Detection in Solar Photovoltaic Arrays Using YOLOv10

The effective management of solar photovoltaic (PV) arrays is vital to maximising energy generation and ensuring long-term performance reliability. The presence of faults, or partial shading, can give rise to hotspots in PV arrays, making their detection critical for timely maintenance and avoiding further impacts on their performance. This paper describes a new approach to the hotspot detection issue in thermal images of solar PV arrays by leveraging deep learning. This study employs the YOLOv10 (You Only Look Once, version 10) model to deliver very high accuracy and speed in identifying and localising hotspots under defined regions of interest (ROIs). The proposed method begins with taking thermal images from multiple solar PV installations while capturing a range of operational conditions and fault types. These images are annotated with hotspot regions to create a robust training dataset. Given YOLO's capability for real-time object detection with high precision and mean average precision (mAP), the YOLOv10 model is then implemented to train on this dataset. Multiple changes were made to the YOLOv10 hyperparameters to optimise them for detecting hotspots in thermal images. The experimental scenario of this paper demonstrates that this approach indeed performs significantly better than standard image processing methods as well as prior deep learning models for detecting both hotspot accuracy as well as speed of processing the images. The YOLOv10 method demonstrated the highest available classification performance with a mAP at intersection over union (IoU) of 0.5 accuracy of 0.91 with inference time suitable for real-time applications. The sample results demonstrated the model's continuous ability to detect hotspots and provide location data under various conditions, such as crossing through different times of day and weather. The results of this study demonstrate that YOLOv10 can be used for improved detection of hotspots in the explicably thermal imagery of solar PV arrays.

Solar photovoltaic module defect detection based on deep learning

Abstract Defect detection for photovoltaic (PV) modules is crucial in their production process, but the dataset quality and complex defects limit the accuracy and speed of the detection. In this paper, a solar PV module defect detection method was investigated using electroluminescence (EL) images. To reduce useless information in the EL images, a PV module segmentation method was proposed to segment PV cells from PV modules. Next, aiming at the insufficient sample size and the imbalance between classes in the dataset, a hybrid data augmentation method was proposed. Then, we proposed an improved YOLOv8n model for PV cell defects with different shapes and small sizes. Experiments showed that the proposed model has good comprehensive performance compared with other SOTA models, with mAP50 reaching 0.943 at only 7.6 G Flops. In addition, the proposed method can complete the defect detection of a PV module EL image containing 144 PV cells within 3 s. Overall, the proposed method meets the requirements of accuracy and real-time detection, providing a feasible solution for defect detection in PV modules.

Performance evaluation of solar photovoltaic system based on perturb and observe (P&O) maximum power point tracking (MPPT) algorithm

The Photovoltaic system (PV) is gaining popularity as a future energy source due to its vast, secure, essentially limitless, and widely accessible nature. However, a prevalent challenge in PV systems is the impact of solar cell radiation intensity and temperature on the output power induced in the photovoltaic module. As a result, monitoring the input source's maximum power point becomes essential for maximizing the effectiveness of the renewable energy system. In order to harvest the most power from the photovoltaic system and channel it to the load through a boost converter, which raises the voltage to the necessary level, most Power Point Tracking, or MPPT, is essential. This study uses the Perturb and Observe (P&O) method to track the photovoltaic module's Maximum Power Point (MPP) and maximize the system's overall power production to overcome this obstacle. By varying the array terminal voltage or current, the P&O approach perturbs the system and then compares the PV output power to the preceding perturbation cycle. Therefore, the main goal of this study is to simulate and assess the effectiveness of the P&O MPPT algorithm in MATLAB/Simulink when it comes to maximizing power extraction from a photovoltaic system. The simulation findings show that the suggested P&O approach is a reliable means of tracking MPP from PV panels, even when there are fluctuations in solar irradiation.

CIGS박막전지를 이용한 컬러BIPV 디자인 현황 및 사례고찰

This study examines the development status of domestic color solar cells and eight domestic and international application sites to overcome the limitations of early-stage BIPV modules that focus only on expanding and distributing power generation and do not meet various building regulations and rules, and to solve somewhat neglected building and cityscape problems. In addition, it analyzes its characteristics to improve the misconceptions and limitations of existing BIPV and to contribute to improving domestic architectural application conditions and future energy policies by reborn as a true product with the function of building exterior materials. In this study, it was found through domestic and international cases that the common feature of BIPV installation cases using CIGS thin-film cells is that BIPV is installed on the entire exterior of a building to meet power generation while pursuing consistency in the design of the exterior, and that CIGS modules satisfy most of the architectural functions that BIPV should have. In addition, although the inevitable efficiency reduction occurs when PV modules have color, it was confirmed that this is a part that must be tolerated to some extent in the comprehensive and combined sense that the ultimate goal of BIPV is to realize sustainable architecture by replacing existing building materials while generating energy. However, there are still many shortcomings in satisfying the color and size required in architecture, and the color that takes into account regional characteristics, so further technological development and advancement are needed.

Asymmetric Multilevel DC Link inverter for Reducing THD using a Meta-Heuristic Algorithm

Multilevel inverters are essential for increasing power quality, boosting efficiency, and lowering harmonic distortion in the field of power electronics. This research presents a new method using the Pelican optimization algorithm (POA) to create pulse patterns in multilevel inverters. The work focuses on deploying a 125-level asymmetric multi-level inverter that is powered by solar panels through a Dc-Dc converter in order to address power quality difficulties. In order to get better performance in multilevel inverter systems, the Pelican optimization algorithm is used to create pulse patterns that are modeled after the hunting strategies of pelicans. The Distributed Static Synchronous Compensator, is a leading device that uses power electronic components to control power flow and improve power quality in power grids. It is one of the many specialized power devices available. The primary objective is to enhance reactive power to ensure the stability of voltage within the power system and the aim of this research is to maintain voltage stability. Using the pelican optimization algorithm, the method entails creating and deploying a DSTATCOM based on a 125-level asymmetric multi-level inverter.

Optimal solution for PV integrated with smart grid connected fast charging EV station: A case study for Jaipur

Current research study investigates the optimal solution for integrating photovoltaic (PV) solar panels with a smart grid-connected fast-charging electric vehicle (EV) station in Jaipur, India, using HOMER software for simulation. The study aims to develop a cost-effective and sustainable charging infrastructure specifically designed for e-rickshaws and personal vehicles, targeting reduced electricity costs. The findings demonstrate that a hybrid Solar and Grid-integrated EV charging station significantly lowers operational costs while promoting renewable energy adoption. The optimized Solar + grid system, compared to a traditional grid-only model, achieves substantial savings, with an average annual energy bill reduction of ₹26,17,806. Despite an initial capital expenditure of ₹50,22,895, the system's financial viability is affirmed by a rapid payback period of 1.9 years (simple) or 2.1 years (discounted) and an impressive internal rate of return (IRR) of 51.3%. Over a 25-year project lifespan, the system is projected to generate total savings of ₹6,54,45,150. The analysis considers various financial metrics, including monthly and annual energy costs, demand charges, and fixed charges, providing a comprehensive overview of the economic benefits. The results underline the importance of incorporating solar power into EV charging stations to reduce overall energy expenses and enhance long-term sustainability. This study offers valuable insights for policymakers, energy managers, and stakeholders, highlighting the economic and environmental advantages of solar-integrated charging solutions in urban settings.

Design and Implementation of a Dual-Axis Solar Tracking System with IoT-Enhanced Monitoring Using Arduino

The position of the sun varies over the day and with the seasons, making it difficult for conventional fixed solar panel systems to achieve optimum energy production. By reorienting the panels to face the sun, solar tracking systems solve this problem. This study suggests a dual-axis solar tracker system that continuously adjusts the panels to stay perpendicular to sunlight in order to maximize energy capture. The system uses sensors to monitor the sun's elevation and azimuth angles. Light sensors, motor controllers, microcontrollers, control algorithms, and an Internet of Things (IoT) monitoring system are some of the system's essential parts. The microprocessor determines the ideal angles for panel alignment based on the location of the sun as detected by the light sensors. The motor driver then adjusts the panels appropriately. The sun's position is accurately and smoothly tracked using dual-axis tracking systems, which employ sensors to detect solar position and actuators controlled by sophisticated algorithms to adjust the panels accordingly. Real-time parameter monitoring of the solar panel is done using an IoT-enabled webpage. The study's findings show that the dual-axis solar tracker system performs noticeably better in terms of energy capture efficiency than fixed installations, especially in areas with high solar incidence angles and fluctuating sunshine.

The 3Cat-4 Spacecraft Thermal Analysis and Thermal Vacuum Test Campaign Results

3Cat-4 is the fourth member of the CubeSat series of UPC’s NanoSat Lab, and it was selected by the ESA Academy’s Fly Your Satellite! program in 2017. This mission aims at demonstrating the capabilities of nano-satellites, and in particular those based in the 1-Unit CubeSat standard, for challenging Earth Observation (EO) using Global Navigation Satellite System-Reflectometry (GNSS-R) and L-band microwave radiometry, as well as for Automatic Identification Systems (AIS). The following study presents the results of the thermal analysis carried out for this mission, evaluating different scenarios, including the most critical cases at both high and low temperatures. The results consider different albedos and orbital parameters in order to establish the optimal temperatures to achieve the best mission performance within the nominal temperatures, and in all operational modes of the satellite. Simulation results are included considering the thermal performance of other materials, such as Kapton, as well as the redesign of the optical properties of the satellite’s solar panels. The correlation with the thermal model and the TVAC test campaign was conducted at the ESA ESEC-GALAXIA facilities in Belgium.

Assessment of short-term supply disruption impacts for the Swiss mobility, energy and ICT sectors – Application of the SPOTTER approach

In this paper, supply disruption impacts along supply chains within the Swiss mobility, energy and ICT sectors are assessed in the short-term (i.e. the next 5 years) using the recently developed SPOTTER approach with the objective to identify hotspots and technologies associated with comparably low supply risks. SPOTTER has been chosen because, compared to other approaches, it is the only one that allows for an assessment along the full supply chain and it is integrated into the Life Cycle Sustainability Assessment framework. To decrease the respective data acquisition efforts for the here-performed supply risk analysis of entire sectors, in a first step, material/product flows that are most influential for an assessment with SPOTTER are identified and considered for the calculation of related impact scores. Our study analyzes critical material and product flows between trade partner countries along relevant supply chains. It thus identifies new hotspots and adds relevant country-specific information to current knowledge. In particular, the identified hotspots refer to volatile prices of solar panels, interrupted trade of oil and gas from Russia, Nigeria and Niger as well as disrupted imports of electronic devices from China. Suggestions for mitigation strategies comprise hedging and/or diversifying the supply chains. Other identified hotspots refer to the critical supply of cobalt and natural graphite along battery supply chains, which could for example be addressed by supporting and/or incentivizing the construction of a circular economy. A third category of identified hotspots indicates risks related to the trade of hafnium and nuclear power plant equipment that could be mitigated by shifting to less risky energy technologies. Our comparisons of overall impacts between relevant technologies suggest higher supply risks for conventional cars, solar panels and Chinese laptops compared to battery electric cars, wind turbines and German laptops. In these comparisons, more details regarding disruptions along full product and fuel supply chains are considered than in current studies.

Compact Solar Generator with Increased Efficiency

Today the world is moving towards renewable energy. Most important and common form of renewable energy is solar energy. But solar panels have less efficiency and covers more space. This paper discusses about new way of generating electricity with the help of solar panel. A small scale experiment was conducted results have proven good performance. In future this concept will prove a golden change in the life of people.

Parameter identification of PV solar cells and modules using bio dynamics grasshopper optimization algorithm

AbstractThe escalating global population and energy demands underscore the critical role of renewable energy sources, particularly solar power, in mitigating environmental degradation caused by traditional fossil fuels. This paper emphasizes the advantages of solar energy, especially photovoltaic (PV) systems, which have become pivotal in hybrid energy systems. However, accurate modelling and identification of PV cell parameters pose challenges, prompting the adoption of meta‐heuristic optimization algorithms. This work explores the limitations of existing algorithms and introduces a novel approach, the bio‐dynamics grasshopper optimization algorithm (BDGOA). The BDGOA addresses deficiencies in both exploration and exploitation phases, exhibiting exceptional convergence speed and efficiency. The algorithm's simplicity, achieved through the implementation of an elimination phase and controlled search space, enhances its performance without intricate calculations. The study evaluates the BDGOA by applying it to identify unknown parameters of five solar modules. The algorithm's effectiveness is demonstrated through the extraction of parameters for RTC France, PWP201, SM55, KC200GT, and SW255 models, validated against experimental data under diverse conditions. The paper concludes with insights into the impact of radiation and temperature on module parameters. The subsequent sections of the paper delve into the intricacies of the PV cell and module model, articulate the formulation of the proposed algorithm, present simulations, and analyse the obtained results. The BDGOA emerges as a promising solution, overcoming the limitations of existing algorithms and contributing significantly to the advancement of accurate and efficient PV cell parameter identification, thereby propelling progress towards a sustainable energy future.