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An agrivoltaic system has been one of the most promising potential sources of clean energy power in green agriculture. Some guidelines have been released to fully meet the requirement of the photovoltaic (PV) modules in agricultural projects. Particularly, it is the high demand for land utilization and mechanization of agricultural production in agrivoltaic systems, such as in Jiangsu and Yunnan provinces in China. Among them, the minimum installation height of PV modules has been standardized to maintain a specific tilt angle. However, the "prioritizing light over agriculture" can often occur during actual production. A large number of PV panels are typically installed on agricultural land in order to ensure the PV power generation in agrivoltaic projects. Furthermore, these PV panels can block the sunlight to form a considerable shading area on agricultural land during the day. This study aims to investigate the effects of shading from PV modules on the internal light environment, shading width, crop yield, and leaf photosynthetic characteristics in the agrivoltaic system. A series of tests were conducted on the agrivoltaic systems at the module installation heights (the vertical distance from the ground to the lower edge of the PV module) of 2.5, 3.2, and 3.9 m in Nanjing City. A systematic measurement was performed on the solar radiation intensity of the planting area under and between the panels inside the PV agricultural system. A quantitative analysis was implemented to calculate the annual variation patterns of the shading width formed by the PV panels. Additionally, there were significant differences in the yield and leaf photosynthetic characteristics of figs (Ficus carica L.) in the planted areas between the modules. The results indicated that the daylighting rate in the planted areas between the panels ranged from 55.4% to 68.9% during the testing period, which was 2.1 to 3.3 times that of the area under the panels. Specifically, the daylighting rate in the areas between the panels decreased at the three heights of PV module installation as the installation height increased, while the area under the panels showed the opposite trend. The shading width caused by the PV modules also increased with latitude and installation height throughout the entire growth period of the figs. Shading from the PV panels led to a reduction in average yield across treatments compared to the CK control, ranging from 19.9% to 48.9%. Additionally, the photosynthetic characteristics of the leaves effectively reflected the yield differences among treatments. A lower installation height of the PV panels can significantly enhance the net photosynthetic rate and stomatal conductance of fig leaves, thereby alleviating the photosynthetic suppression caused by shading. In summary, compared to agrivoltaic systems with panel heights of 3.2 m and 3.9 m, the system with a panel height of 2.5 m offered a better internal light environment and shorter shading width, effectively reducing the decline in fig yield and demonstrating practical value for photovoltaic agricultural projects in various regions. [ABSTRACT FROM AUTHOR]

Copyright of Precious Metals / Guijinshu is the property of Precious Metals Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Deriving an accurate and simple current-voltage (I-V) characteristic for photovoltaic (PV) module is highly significant for condition monitoring, fault detection and maximizing power production in PV systems. Equivalent circuits consist of one or more diodes are mostly utilized for I-V curve modelling. However, these models are inherently implicit, relatively complex and nonlinear in their parameters. Here, a piecewise quadratic function with four different intervals is generated from the measured I-V data at standard test condition (STC). The intervals are chosen such that the best model performance can be achieved, especially at maximum power point. Each quadratic function is obtained from least square technique according to the experimental data in the corresponding interval. It is easy to obtain the voltage value at MPP from the extracted model, analytically. Also, a suggestion is provided for extending the generated I-V model to the real environmental condition by utilization of artificial neural network. The derived PV module model is highly suitable for maximum power point tracking, monitoring and fault detection due to its simplicity, explicit structure and accuracy.

Photovoltaic energy system is one of the most popular and quickly expanding renewable energy sources because of its great availability, enormous potential, and environmental sustainability. In addition, photovoltaic systems produce clean energy and not pollutant thus contributing to the fight against global warming. Photovoltaic system requires less maintenance given the absence of mechanical components, and it can be also integrated to the electrical grid. The objective of this research is proposed an effective method for designing and Modeling of an 11 MWp photovoltaic plant capacity connected to the national grid of Niger by using software PVsyst. Method: choice of the type of system (system connected to the network), the geographic location of the installation site using the software database. Then comes the orientation of the photovoltaic system, the choice of the technology of the photovoltaic system (photovoltaic panels, inverters, transformers... etc) and their calibrations, Study of detailed losses (system losses, wiring losses and array losses) and execution of the command to simulate the system. Results: design and simulation model of an 11MWp photovoltaic power plant was developed composed of 29120 photovoltaic panels with a power of 385 Wp each, 133 MPPT inverters with a power of 66 KWac each and a Medium Voltage transformer with a capacity of 11 MW. The project has been simulated and results obtained has been analyzed. Practical significance of the research: Design and Simulation of photovoltaic system using the software allows the engineers, academics and researchers to acquire skills in the design and simulation of photovoltaic systems.

Abstract Solar energy plays an important role in renewable energy generation, with the advantages of low pollution, easy installation, and relatively easy access. However, photovoltaic (PV) modules are susceptible to cause localized shading from external factors such as leaves in the canopy, surrounding buildings, etc., which would affect power generation efficiency and even pose safety risks. Existing methods cannot perform well in real-time conditions. This paper proposes a real-time shading monitoring method for the PV module based on computer vision. The gamma transform and histogram matching were adopted to enhance key features and adjust the global gamut strength distribution in the image of the PV module; then the gray-level slicing method finished the segmentation to detect the shadow from the video. All processing can be realized in the real-time monitor camera and the detection results can be displayed on the HMI in PC with high efficiency and low cost. According to tests in the practical complex environment, the method can have enough detection performance and high real-time performance with an accuracy of 0.98, and the F0.5 and F2 values are 0.87 and 0.85, respectively. The metrics of the proposed method are higher than those of the existing Canny detection method, the Random Forest detection method, and the CNN detection method. In addition, the average time required by the proposed method to process a frame is 0.721 s. In addition, the average time required by the method to process an image frame is 0.721 s, which has good real-time performance.

The article presents a description of the modeling and manufacturing of solar modules of photovoltaic, thermal and photovoltaic thermal designs intended to supply energy to a biogas plant intended for the production of biohydrogen and biomethane. Mathematical modeling of the photovoltaic module was carried out and the calculated temperature of the photovoltaic converters was determined, and also the photovoltaic module was manufactured using the process of sealing the photovoltaic converters with a two-component polysiloxane compound. Mathematical modeling of a solar thermal collector with water radiators in the form of a round copper tube, an oval copper tube, as well as a rectangular water cooling channel ("water jacket") was also carried out, in which pressure losses and the amount of water heating were assessed. The distances between the rectangular channels of the water-cooling radiator for uniform heat removal were determined, and mathematical modeling of the thermal state of a solar thermal collector with a semicircular cross-section of the water-cooling radiator channel was carried out, after which the solar thermal collector was manufactured. Mathematical modeling of the total displacements of the components of a photovoltaic thermal module, the values of which do not exceed acceptable limits, was carried out, after which a photovoltaic thermal module was manufactured using the process of sealing photovoltaic converters with a two-component polysiloxane compound. [ABSTRACT FROM AUTHOR]

Solar energy plays an important role in renewable energy generation, with the advantages of low pollution, easy installation, and relatively easy access. However, photovoltaic (PV) modules are susceptible to cause localized shading from external factors such as leaves in the canopy, surrounding buildings, etc., which would affect power generation efficiency and even pose safety risks. Existing methods cannot perform well in real-time conditions. This paper proposes a real-time shading monitoring method for the PV module based on computer vision. The gamma transform and histogram matching were adopted to enhance key features and adjust the global gamut strength distribution in the image of the PV module; then the gray-level slicing method finished the segmentation to detect the shadow from the video. All processing can be realized in the real-time monitor camera and the detection results can be displayed on the HMI in PC with high efficiency and low cost. According to tests in the practical complex environment, the method can have enough detection performance and high real-time performance with an accuracy of 0.98, and the F0.5 and F2 values are 0.87 and 0.85, respectively. The metrics of the proposed method are higher than those of the existing Canny detection method, the Random Forest detection method, and the CNN detection method. In addition, the average time required by the proposed method to process a frame is 0.721 s. In addition, the average time required by the method to process an image frame is 0.721 s, which has good real-time performance. [ABSTRACT FROM AUTHOR]

This study introduces the CP-EODE algorithm, a novel hybrid of the Equilibrium Optimizer (EO), and the Differential Evolution (DE) algorithm. It addresses EO's tendency toward premature convergence by enhancing its exploration capabilities. The motivation for this research stems from the need for more efficient and economically viable designs in engineering, particularly in the optimization of shell-and-tube heat exchangers (STHEs) and photovoltaic module parameters. The CP-EODE algorithm leverages DE's exploration strengths to enhance EO's performance adopting a cooperative parallel approach that divides the population into EO and DE sub-populations for a more effective search space exploration. The validation of CP-EODE's effectiveness commenced with its application to 29 benchmark functions, where a Friedman test yielded an F r value of 22.19, significantly surpassing the critical value of 7.81, and a Wilcoxon signed rank test confirmed statistical improvements with a z-score beyond − 1.96. Four quality metrics were discussed to provide a detailed assessment of CP-EODE's performance highlighting its robust exploration and exploitation capabilities. In practical applications, CP-EODE demonstrated significant advancements in STHE design achieving up to 19.706% reduction in heat exchanger surface area compared to traditional and other algorithmic designs, showcasing its potential to enhance engineering design efficiency and cost-effectiveness. A critical examination of the algorithm's convergence rate revealed its swift optimization capability, with rapid attainment of optimal solutions in various test cases, further establishing CP-EODE's advantage in handling complex optimization challenges. Through rigorous statistical analysis and performance metrics, CP-EODE emerges as a superior solution for complex optimization challenges, underscoring the value of hybrid algorithmic strategies in engineering optimizations. [ABSTRACT FROM AUTHOR]

The occurrence of partial shading in solar power systems presents a substantial challenge with widespread implications, sparking extensive research, notably in the field of maximum power point tracking (MPPT). This study emphasizes the critical process of accurately tracking the maximum power points with the characteristic curves of photovoltaic (PV) modules under real-time, diverse partial shading patterns. It explores the various stages of the tracking process and the methodologies employed for optimization. While conventional methods show effectiveness, they often fall short in swiftly and accurately tracking maximum power points with minimal errors. To address this limitation, this research introduces a novel machine learning approach known as adaptive reinforcement learning with neural network architecture (ARL-NNA) for MPPT. The results obtained from ARL-NNA are compared with existing algorithms using the same experimental data. Furthermore, the outcomes are validated through different factors and processing time measurements. The findings conclusively demonstrate the efficacy and superiority of the proposed algorithm in effectively tracking maximum power points in PV characteristic curves, providing a promising solution for optimizing solar energy generation in partial shading patterns. This study significantly impacts various realms of electrical engineering including power engineering, power electronics, industrial electronics, solid-state electronics, energy technology, and other related field of engineering and technology. [ABSTRACT FROM AUTHOR]

In the work the current-voltage characteristic (CVC) of photovoltaic modules (PVM) were measured by fast scanning them using an automated system developed by us on the Raspberry Pi microcomputer platform. A theoretical model and an algorithm for processing experimental data have been developed for the analysis of PVM parameters changes with voltage. The voltage dependences of the ideality factor of the pn junction, the reverse saturation current and their influence on the CVC have been clarified. Numerical determination of these parameters allows characterizing electrical losses and recombination processes in solar cells under various external conditions. It is shown that ideality factor can be significantly exceeds 2 in the voltage range less than the maximum power point. This is characteristic for PVM manufactured using modern technologies. The changes of the reverse saturation current and its growth in a certain voltage range due to significant recombination current were analyzed. [ABSTRACT FROM AUTHOR]

This work explores the possibility of expanding the use of solar energy potential for green hydrogen production in Ukraine using photovoltaic stations as the primary energy source. The increasing popularity of electric vehicles, such as traditional battery electric vehicles, hybrid and fuel cell electric vehicles, creates a demand for efficient green hydrogen refueling systems. The authors analyze various implementation options of autonomous photovoltaic hydrogen production systems, including schemes with one or two buffer batteries to guarantee the needs of fuel cell vehicles. The importance of using renewable energy sources to compensate for irregular hydrogen production and ensure continuous supply for electric vehicles is highlighted. The results of the calculations show that the implementation of such systems is reasonable and promising for the development of eco-friendly transport in Ukraine. The study provides justification for the necessary capacity of charging station and the volume of hy-drogen storage to optimize hydrogen production and consumption in variable energy situations. [ABSTRACT FROM AUTHOR]

This investigation is a preliminary study to analyze the impact of cooling system applications on photovoltaic module (PVM) performance. This study aims to determine the PVM specifications before being tested using a cooling system and compare its performance. This research is important because manufacturers do not provide information on the performance operating under irradiance. The test was conducted at a height of 887 m above sea level, with a tilt angle of 11°, facing North direction, with a specific coordinate location of 6°51’58.63″ S and 107°35’26.20" E. Two 120-watt PVM made of monocrystalline and polycrystalline materials were subjected to a six-day testing period. The on-site measurements took place between July 25 and July 30, 2023, starting at approximately 6:00 and ending at 18:00. The measurement for six days exhibited an average irradiance of 501 ± 23 W(max. 973 W); the average temperature of 43.1 ± 0.8 °C (max. 62.3 °C); the PVM generated an average power output of 32.5 ± 1.3 W and 36.8 ± 1.6 W for mono-PVM and poly-PVM, respectively. Furthermore, it showed an efficiency of 12.5 ± 0.1% for mono-PVM and 13.4 ± 0.2% for poly-PVM. However, the average performance of the PVM was not significantly different based on the independent samples t-test method.

This paper addresses a multi-objective particle swarm optimization (MOPSO) algorithm to locate the best position at which a single distributed generator (DG) with optimum voltage amplitude and phase can be installed. The objective is to maximize the power factor and voltage in IEEE 9-Bus system and voltage in IEEE 15-Bus system using MATLAB/SIMULINK software. Systematic simulations revealed that a single DG at Bus-5 with voltage amplitude/phase of 230 kV/-13∘ caused highest objective function value of 1.9919 with an improvement in power factor and voltage of 1.39% and 1.15% respectively. In IEEE 15-Bus system, DG at Bus-3 caused the highest objective function (0.8828) with DG voltage amplitude/phase of 10.735 kV/23.12∘ and 11.89% voltage improvement. Finally, a robust control program in dq0 domain is developed to achieve calculated powers from photovoltaic generator at DG bus whose effect is validated to be the same at all buses as given by the MOPSO algorithm.

Photovoltaic (PV) modules must be monitored and evaluated in real-time during long-term operations to maintain a high performance, and series resistance is a critical component of this process. However, existing series resistance extraction methods may have either low accuracy or a very complex solution process. Worth mentioning, the size of this series resistance depends on environmental conditions, and the extraction results under different environments cannot be mutually referenced and compared. In this study, an analytical method for series resistance was derived, and a correction method for I-V curves that differs from the IEC 60891 standard was proposed, based on the derivative of the I-V characteristic curve of PV modules. The proposed analytical method was compared with three other methods for PV modules with three varied materials, and the influence of the ideality factor on parameter accuracy was also assessed. The obtained results showed that the proposed method can achieve high accuracy through an analytical expression. In addition, the I-V curves of a PV module operating for more than 6 years were corrected to fit the expected environmental conditions via the proposed correction method. Upon the calculated average value of the series resistance, the results indicated that the PV module has slightly aged, which verifies the effectiveness of the recommended method. [ABSTRACT FROM AUTHOR]

Organic photovoltaic (OPV) technology has recently achieved remarkable progress in academia, attaining power conversion efficiencies exceeding 19%, a breakthrough previously unimaginable. Despite these advancements, the translation of high‐efficiency OPV cells into the commercialization of OPV modules has been limited, impeding the market penetration of OPV technology. Upon careful examination, various challenges emerge, including disparities between laboratory research and industrial production in device architecture, influenced by considerations of cost and manufacturability. Additionally, the incorporation of high‐efficiency materials requires careful consideration not only of the compatibility between the photoactive layer and interface layers or transparent electrodes but also of the associated synthetic complexity of chemical structures and cost implications. While these issues are crucial, they may not be widely discussed in current mainstream research. In this article, the purpose is to identify bottlenecks from a material techniques perspective, with the aim of promoting increased resources in the research of pertinent module technologies. This effort is intended to propel OPV technology toward greater success and bring it into closer alignment with the goals of commercialization. [ABSTRACT FROM AUTHOR]

Lightning surges are one of the major reasons of bypass diode (BPD) failure in photovoltaic modules. In this study, standard lightning impulse voltages (SLIVs) were directly applied to Schottky barrier diodes (SKB diodes) and rectifier diodes (PNJ diodes) with different electrical specifications. The critical voltages Vc of SLIV at which the diodes were damaged and the temporal variations of diode current (iD) and voltage (vD) were measured. It was found the iD‐vD characteristics can be categorized into a total of eight patterns: four patterns for the forward SLIV application and the four for the reverse SLIV application, regardless of diode types. The measured results imply that the irreversible voltage dip that occurs before the termination of the reverse breakdown contributes significantly to diode damage in the both forward and reverse SLIV applications. SKB diodes exposed to the reverse SLIV are easily damaged at the lowest magnitude of SLIV among all combinations of diode types and SLIV directions. Once a diode has been damaged due to the first SLIV application, the second application of SLIV to the same diode causes remarkable change in the iD‐vD characteristic even if the magnitude of the second SLIV is smaller than Vc. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The object of this study is photovoltaic modules with different seasonal tilt angles at different geographical latitudes. The average annual efficiency of photovoltaic modules with different seasonal angles set at different geographical latitudes has been determined as the annual weighted average value of the cosine of the angle of incidence of solar rays on the plane of the photovoltaic module. The influence of seasonal tilt angles of photovoltaic modules at different geographical latitudes on their average annual efficiency was analyzed. Approximate values of the seasonal tilt angles of photovoltaic modules at different geographic latitudes take values that differ from the geographic latitude value by plus 15° for the winter period and minus 15° for the summer period. Modeling the average annual efficiency of photovoltaic modules depending on the seasonal tilt angles at different geographical latitudes made it possible to obtain refined values of the seasonal tilt angles of photovoltaic modules. Thus, at the latitude of 0°, 10°, 20°, 30°, 40°, 50°, and 60°, the tilt angle of photovoltaic modules for the winter period will be 14.8°, 24.6°, 34.5°, 44.4°, 54.1°, 63.6°, and 73°, respectively, and for summer – minus 14.5°, minus 4.6°, 5°, 15.1°, 25.1°, 34.9°, and 44.7°. Dependences were obtained for determining the seasonal tilt angles of photovoltaic modules depending on the value of geographic latitude. The difference in the average annual efficiency of photovoltaic modules, which are installed at seasonal angles, and photovoltaic modules, which track the position of the Sun in the vertical plane, is 0.4 %. The results could be used as a basis for evaluating the efficiency of photovoltaic modules when determining the seasonal tilt angle at different geographic latitudes. [ABSTRACT FROM AUTHOR]

Everywhere in the globe, the total capacity of photovoltaic (PV) panels is expanding at an exponential rate. Arc faults, open-circuit (OC) faults, bypass diode failures, mismatch faults, and short circuit faults are only a few of the most common types of problems that may occur in PV arrays. Not recognizing and correcting these issues quickly might affect power plant production. Fault detection in PV modules helps stabilize PV plant output. Machine learning techniques can automatically identify PV module issues. This paper portrayed fault detection using Multi-Layer Perceptron (MLP) and Recurrent Neural Network (RNN). Two methods identify PV defects based on normalizing factors. MLP has nonlinear problems and is slow to compute. The suggested RNN proved to be a superior detection approach for 10 weeks of testing on 2.4 KW monocrystalline solar panels. MLP has 75.62% fault detection accuracy whereas RNN has 98.95% in 4s-2p PV panels. Therefore, the findings of the simulation indicate that the proposed RNN technique achieves the necessary level of speed and accuracy. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Xinyang Normal University Natural Science Edition is the property of Journal of Xinyang Normal University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

BackgroundPhotovoltaic (PV) cells can convert solar energy into electricity and alleviate the dilemma of energy supply shortage. Shanghai's PV module industry is booming, but there are few reports on the health status of the workers and there is an urgent need for health risk assessment. ObjectiveTo evaluate the health status of PV module production workers and the association between work and health status, in order to provide a direction for the health management of the workers. MethodsAmong the PV module production workers who completed prescriptive occupational health examination by a designated medical institution in 2021, 2453 workers with more than one year of working age were selected as the exposure group and 538 newly hired workers as the control group. On the basis of the Technical specifications for occupational health surveillance (GBZ 188−2014), the health examination included physical examination and laboratory examination and information such as sociodemographics, living habits, and disease history. We compared the indicators of pure-tone hearing test, blood routine examination, electrocardiogram (ECG), liver function, and kidney function between the two groups. The blood routine results included erythrocyte-related indicators, leukocyte-related indicators, and platelet-related indicators, and the results of liver function included hepatocyte injury indicators, hepatic secretory function indicators, and hepatic synthesis function indicators. The workers were divided into four groups by quartiles of working age. Trend chi-square test was used to analyze differences in health status between the four working age groups. Multiple logistic regression models were used to evaluate the association between working age and health indicators. ResultsAmong the exposure group workers, 2035 (83.0%) were male and 418 (17.0%) were female. The median (P25, P75) age was 34.0 (30.0, 39.0) years and the median (P25, P75) working age was 6.0 (3.0, 10.0) years. The abnormality rate of blood routine was 61.7%. Among them, the abnormality rates of platelet-related indicators and liver secretory function indicators were 39.8% and 48.1% respectively. The risks of abnormal hepatocyte injury-related indicators, fatty liver, and platelet-related indicator abnormalities among the exposure group were 1.471 (95%CI: 1.060, 2.054), 1.691 (95%CI: 1.208, 2.385), and 7.576 (95%CI: 4.967, 11.994) times higher than those in the control group respectively. The single-factor analysis demonstrated a positively linear trend between working age and hypertension prevalence, electrical audiometry abnormality rate, or liver secretory function indicator abnormality rate. Corrected for gender, age, smoking status, hypertension, etc., the results of logistic analysis showed that quartile working age was positively related to abnormal liver secretion function and abnormal platelet-related indicators respectively (OR=1.047, P=0.005; OR=1.037, P=0.014), and inversely associated with the abnormal rate of renal function (OR=0.953, P=0.044). ConclusionNegative associations between health status and working age are identified in PV module production workers. The target PV module production employees are in younger age, and with the increase of working age, the abnormalities of liver function and platelets may increase. Therefore, the enterprises should extend occupational health work from workplace to workers.

Abstract The use of a reflector can increase the solar radiation on the photovoltaic module (PV) surface, whereby the energy output can be improved. However, the economic feasibility may need to be considered too. This study is conducted, for the first time, due to the lack of studies regarding the economic feasibility assessment of implementing reflectors under the Malaysian meteorological conditions. The outcome will give information about the suitability for implementing a PV reflector in Malaysia through an experimental setup at a sewage treatment site, for two months in 2022. The Malaysian meteorological data, which include daily solar radiation, ambient temperature and wind velocity, were collected to study the output energy, efficiency and the economic perspective of a PV. In February 2022, the PV was operating without a reflector and the averaged values for the monthly solar radiation, ambient temperature and wind velocity were 539.9 MJ/m2, 28.4 °C and 2.2 m/s, respectively, which resulted in an output energy of 106.43 kWh. On the other hand, for April 2022, the PV was operating with a reflector. With the respective averaged input parameters 544.98 MJ/m2, 28.9 °C and 1.51 m/s, the output energy was 121.94 kWh. It is thus shown that the PV with a reflector increases the PV’s output energy by 14.57%. Also, it is shown that the cost-effective factor value is 0.955 which means that the PV reflector is economically feasible to be implemented under the Malaysian meteorological conditions. Hence, extensive research should be conducted to improve the performance of PV reflectors. The findings of this paper maybe useful for researchers and/or manufacturers of PV reflectors.

The object of this study is photovoltaic modules with different options for the angle of their installation to the horizon at different geographical latitudes. The scientific problem to solve was determining the dependence of the average annual efficiency of solar photovoltaic modules on the mounting angle of photovoltaic modules and the value of geographical latitude. It has been proven that the efficiency of installation of solar photovoltaic modules can be increased by reducing the angle of their inclination to the horizon depending on the value of the geographical latitude at which they are installed. The average annual efficiency of photovoltaic modules with different mounting angles to the horizon at different geographical latitudes was determined as the annual weighted average value of the cosine of the angle of incidence of solar rays on the plane of the photovoltaic module. The maximum of the average annual efficiency of photovoltaic modules corresponds to a smaller value of the angle of their installation to the horizon than the value of the geographical latitude. So, with a latitude value of 10°, 20°, 30°, 40°, 50°, and 60°, the mounting angle of photovoltaic modules to the horizon will be 9.5°, 18.8°, 28°, 37°, 45.8°, and 54°, respectively. A dependence was derived that allows determining the mounting angle of photovoltaic modules to the horizon dependent on the value of the geographical latitude at which they are installed. A mathematical expression was also constructed that makes it possible to determine the average annual efficiency of photovoltaic modules depending on the angle of their installation to the horizon for different values of geographic latitude. The results could be used in calculating the average annual efficiency of photovoltaic modules based on the adjusted values of the angle of their installation to the horizon at different geographical latitudes. [ABSTRACT FROM AUTHOR]

Forecasting the generation of electrical energy is mandatory for grid photoelectric plants. According to the requirements of the energy market, the hourly production of electricity for a day in advance is subject to forecasting, which is determined by the terms of purchase/sale of electricity. This forecasting is performed using predictive models of photovoltaic solar energy conversion processes. Such a model usually includes two components: the first of which is a purely analytical, deterministic model that takes into account the relative position of the Sun and the geolocation points of the photovoltaic station at the time of the forecast, and the second is a stochastic model with machine learning, which, based on the results of the training, builds a function of the influence of meteorological factors on level of electricity generation. But, as the results of the tests showed, a significant drawback of this approach, which significantly worsens the forecast results, is the failure to take into account the effect of thermal reduction of the power of the photovoltaic plant. In order to improve the prediction model of a photovoltaic plant, taking into account the effect of thermal reduction of its power at a supercritical increase in the temperature of its panels, a simulation model of the effect of thermal reduction of the power of photovoltaic modules, which make up the working panels of photovoltaic plants, was built and studied. Special attention is paid in this paper to the identification and study of the dominant meteorological factors, influencing the thermal mode of photovoltaic modules. According to the results of the research, the third component of the prediction model was built, which takes into consideration the effect of thermal reduction of photovoltaic modules power. The results of the study of the revised three-component prediction model of the grid photoelectric station for the forecasting the hourly production of electric energy a day in advance showed a significant decrease in the imbalance between the predicted and actually produced energy. [ABSTRACT FROM AUTHOR]

Aiming at the problem of identifying the characteristics of ash accumulation and shadow of photovoltaic modules, the difference of photovoltaic characteristic curves of ash accumulation and shadow was analyzed in detail, and the time-varying characteristics of the inflection point of the shadow photovoltaic curve were revealed. The number of inflection points of the characteristic curve and the current and voltage characteristic conditions were proposed to form the input feature quantity of the training model, and the dust accumulation and shadow recognition model was trained based on the CatBoost algorithm. Finally, the performance analysis and comparative test of the recognition model trained by CatBoost algorithm, ID3 and GA-BP algorithm were carried out by using the measured data of photovoltaic modules, and the results show that the recognition model trained based on CatBoost has strong discrimination and high diagnostic accuracy, which is of great engineering application value. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) modules are highly efficient power generators associated with solar energy. The rapid growth of the PV industry will lead to a sharp increase in the waste generated from PV panels. However, electro-waste can be successfully used as a source of secondary materials. In this study, a unique procedure for recycling PV modules was developed. In the first stage, the aluminum frame and junction box, 18wt%. and 1wt%. of the module, respectively, were removed. The following stage was crucial, involving a mechanical–thermal method to remove the glass, which accounts for 70wt%. As a result, only 11wt%. of the initial mass of the PV was subjected to the next stage of chemical delamination, which reduced the amount of solvent used. Toluene was used to swell the ethylene vinyl acetate, EVA, and allow for the separation of the PV module. The effects of temperature and ultrasound on separation time were investigated. After the separation of silicon cells, metal ribbons, EVA, and the backsheet were obtained. The purity of the polymers was determined by FTIR and elemental analysis. Thermal properties were measured using DSC calorimetry to determine the basic parameters of the material. [ABSTRACT FROM AUTHOR]

We investigated the influence of the pre-application of reverse bias on the potential-induced degradation (PID) of n-type front-emitter (n-FE) crystalline Si (c-Si) photovoltaic modules. Applying a prior positive reverse bias to n-FE cells delays charge-accumulation-type PID (PID-1), decreases in short-circuit current density (J sc) and open-circuit voltage (V oc). The prior positive bias accumulation may accumulate negative charges in the SiN x , which leads to an increase in a duration for the positive charge accumulation by the PID test applying a negative bias. We also found that sufficiently long prior positive bias application results in the delay of PID-2, a fill factor reduction by the incursion of Na ions into the depletion region of the p–n junction of n-FE cells. [ABSTRACT FROM AUTHOR]

The integration of photovoltaic (PV) and thermoelectric (TE) modules in PV-TE systems has shown potential for expanding the utilization of the solar spectrum, enhancing the total power output, and reducing the space that is required for PV power plants. This paper discusses the characteristics of a practical PV-TE system model. Typically, to boost the power output of the TE component, a significant temperature difference is induced across the thermoelectric generator (TEG) module using various heat removal methods. These cooling techniques not only enhance the TEG module's efficiency but may also improve the performance of the PV component. In this study, we evaluate the efficiencies of PV-TE systems that are equipped with polycrystalline silicon solar cells and seven distinct TEGs under four different conditions. Initially, the PV-TE hybrid systems are tested without a cooling mechanism at an ambient temperature of 25 °C (Standard Test Conditions EN/IEC 61215). Subsequently, we examine the systems with a passive cooling approach, employing aluminum heat sinks to facilitate improved heat dissipation. Further tests involve an active cooling system using water and then nanofluid as coolants. The results from these assessments aim to establish a benchmark for enhancing the efficiency of future PV-TE systems. [ABSTRACT FROM AUTHOR]

In this research, hybrid method is proposed to model the I–V characteristic curve of a photovoltaic (PV) module. The method is represented by a multi-objective arithmetic optimization and cuckoo search with multi-criteria decision-making approach. The proposed model generates first a number of I–V curves as candidates. This phase is conducted through multi-objective optimization algorithm. The optimization algorithm is assessed by a non-dominated ranking scheme and crowding distance framework. After that, the best I–V curve candidate is chosen from the result of Pareto front by using the VIKOR multi-criteria decision-making method. Moreover, the analytic hierarchy approach is employed to select the appropriate weight for each criterion. The proposed method is validated by using an experimental data under various operational conditions. This validation is done by extracting different I–V characteristic for PV modules. The proposed method is compared to a number of methods in the literature. Results show that the proposed method exceeds other methods in the literature considering the accuracy of generating the I–V curves. In addition, results show that the proposed method requires less computational power as compared to other hybridized methods. [ABSTRACT FROM AUTHOR]

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Solar energy is one of the advanced and most widespread types of renewable energy sources, which is developing at a rapid pace. In the process of this development, certain standards and approaches to the calculation and formation of the configuration of solar power plants are being formed. One of these approaches is the formation of an array of photovoltaic modules, the total power of which is greater than the total power of the inverters. The article considered the main reasons for applying the approach of overloading inverters with generating units in photovoltaic systems. The PVsyst software environment was used to simulate the production of electrical energy for solar power plants with an increased installed capacity of photomodules and equal inverter capacity for a comparative assessment of the feasibility of using such an approach. The payback period of additional investments, which are necessary to achieve conformity between the total power of the array of photovoltaic modules and the power of inverters, is determined by using technical and economic calculation. All results were visually demonstrated by constructing two-dimensional and three-dimensional dependences. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) modules must be monitored and evaluated in real-time during long-term operations to maintain a high performance, and series resistance is a critical component of this process. However, existing series resistance extraction methods may have either low accuracy or a very complex solution process. Worth mentioning, the size of this series resistance depends on environmental conditions, and the extraction results under different environments cannot be mutually referenced and compared. In this study, an analytical method for series resistance was derived, and a correction method for I–V curves that differs from the IEC 60891 standard was proposed, based on the derivative of the I–V characteristic curve of PV modules. The proposed analytical method was compared with three other methods for PV modules with three varied materials, and the influence of the ideality factor on parameter accuracy was also assessed. The obtained results showed that the proposed method can achieve high accuracy through an analytical expression. In addition, the I–V curves of a PV module operating for more than 6 years were corrected to fit the expected environmental conditions via the proposed correction method. Upon the calculated average value of the series resistance, the results indicated that the PV module has slightly aged, which verifies the effectiveness of the recommended method.

This research facilitates the placement of solar and wind-powered distributed generators (RDGs) in an unbalanced 3-phase IEEE 25-node distribution system intended to minimize the total expense of buying electricity from the grid and releasing carbon dioxide over twenty years. The probability distribution functions of Beta and Rayleigh are employed to attain solar illumination and wind velocity in a particular region. Unbalanced voltage deviation (UVD) and harmonic flows (HFs) from non-ideal loads and RDGs are considered. Metaheuristic methods are coded in MATLAB to find RDGs' location and capacity, and then OpenDSS is used to calculate power and harmonic issues. As a result, the total expenses can be reduced to $1.4141 million, which is 10.7% of the base system’s. Additionally, HFs and UVDs at every node follow the IEEE Std. 519 and the IEEE Std. 45-2002, respectively. So, the placement of RDGs has a beneficial position in unbalanced distribution systems.

This research addresses the pressing need for clean energy solutions by focusing on the increasing adoption of photovoltaic (PV) modules as alternatives to fossil fuel-based energy sources. Despite their promise, PV modules face challenges in maintaining efficiency under varying environmental conditions. To tackle this, the study develops a one-diode model of PV modules using the Gravitational Search Algorithm (GSA) to obtain the optimal PV parameters so that the performance of the PV modules across diverse weather conditions can be obtained. The integration of a microcontroller and sensors in the experimental setup allows for real-time monitoring of critical environmental variables such as irradiance, temperature, and humidity, alongside voltage and current measurements. While there are certain constraints, such as the sensitivity of sensor data to weather conditions and the significant dependency of estimated parameters on measurements. However, by analyzing the performance of three specific PV modules SOLTECH215, PHOTOWATT220, and KC200GT under varying conditions, this research provides invaluable insights into optimizing energy production and efficiency in practical applications. By bridging theoretical modeling with experimental validation, it lays the groundwork for more efficient and reliable solar energy systems, thus driving the transition towards a cleaner and more sustainable energy future.

A large part of incident solar radiation on photovoltaic (PV) modules is converted into heat, leading to overheating and reduction of PV modules performance. The present work investigates the impact of rectangular aluminium fins (RAFs) and evaporative cooling represented by cotton wicks immersed water (CWWs) on the performance and thermal behaviour of the PV module. Results indicate that the evaporative cooling attained better cooling potential than RAFs, in which the PV module temperature was reduced by 22.3%, and the output power was enhanced by 73% thanks to continuous cooling of the PV module. A slight improvement in the PV module performance was observed with RAFs due to the increased heat transfer area, which reduced temperature by up to 6.7% and increased the output power of the PV module by up to 21.3 %. Exergy analysis shows a gradual increment of the electrical exergy and exergy efficiency using CWWs, which reduces the entropy generation of it more than RAFs. The study concluded that PV modules without cooling in hot climate areas may deteriorate their performance significantly.

While traditional methods for modelling the thermal and electrical behaviour of photovoltaic (PV) modules rely on analytical and empirical techniques, machine learning is gaining interest as a way to reduce the time, expertise, and tools required by designers or experts while maintaining high accuracy and reliability. This research presents a data-driven machine learning tool based on artificial neural networks (ANNs) that can forecast yearly PV electricity directly at the optimal PV inclination angle without geographic restrictions and is valid for a wide range of electrical characteristics of PV modules. Additionally, empirical correlations were developed to easily determine the optimal PV inclination angle worldwide. The ANN algorithm, developed in Matlab, systematically and quantitatively summarizes the behaviour of eight PV modules in 48 worldwide climatic conditions. The algorithm’s applicability and robustness were proven by considering two different PV modules in the same 48 locations. Yearly climatic variables and electrical/thermal PV module parameters serve as input training data. The yearly PV electricity is derived using dynamic simulations in the TRNSYS environment, which is a simulation program primarily and extensively used in the fields of renewable energy engineering and building simulation for passive as well as active solar design. Multiple performance metrics validate that the ANN-based machine learning tool demonstrates high reliability and accuracy in the PV energy production forecasting for all weather conditions and PV module characteristics. In particular, by using 20 neurons, the highest value of R-square of 0.9797 and the lowest values of the root mean square error and coefficient of variance of 14.67 kWh and 3.8%, respectively, were obtained in the training phase. This high accuracy was confirmed in the ANN validation phase considering other PV modules. An R-square of 0.9218 and values of the root mean square error and coefficient of variance of 31.95 kWh and 7.8%, respectively, were obtained.The results demonstrate the algorithm’s vast potential to enhance the worldwide diffusion and economic growth of solar energy, aligned with the seventh sustainable development goal.

The current work proposes a novel method for photovoltaic (PV) modules’ performance estimation at various weather conditions based on the extraction of the single-diode model’s five parameters. The new modeling technique is not built on any approximations or iterative processes, and this makes the approach combining two substantial benefits in the PV simulation field which are the accuracy of prediction and the rapidity of convergence. Moreover, the method is founded on the resolution of matrix equations based on reduced forms, and that assures its simplicity by avoiding tedious calculations. Indeed, only the mean points of the current–voltage characteristic are employed to get the values of the five parameters by calculating two of them using a numerical resolution of a two non-linear equations’ system. Then, the expressions giving the three last parameters are determined as functions of the two first parameters and regrouped in a matrix equation, which is solved analytically to get the values of these remaining parameters. The productiveness of the proposed method is tested using PV modules of various technologies and proved using eight well-known simulating methods for the one-diode model. Furthermore, the root mean squared errors matching the new method have not exceeded 0.093 A and the obtained convergence time is less than 0.27 s. The results attest to the relevance of the new method for dynamic applications and for PV designers requiring simple modeling techniques.