Shadow Conditions Research Articles

Photovoltaic Maximum Power Point Tracking Technology Based on Improved Perturbation Observation Method and Backstepping Algorithm

Photovoltaic power generation systems mainly use the maximum power tracking (MPPT) controller to adjust the voltage and current of the solar cells in the photovoltaic array, so that the photovoltaic array runs at the maximum power point (MPP) to achieve the purpose of maximum power output. At present, photovoltaic power stations mainly adopt the traditional method to track the maximum power point, but this fixed step method easily causes output power oscillation of the photovoltaic array when tracking the maximum power point, and it easily falls into the local extreme point under partial shadow conditions. In order to solve these problems, this paper proposes an improved perturbation observation method and backstepping method (IP&O-backstepping) to replace the traditional method applied to the MPPT controller to optimize the operating state of the solar cell, thereby improving the output power point of the photovoltaic array and increasing the output power of the photovoltaic array. The algorithm first uses the improved perturbation and observation (IP&O) method to search the maximum power point of the photovoltaic array and output the reference voltage. Secondly, the reference voltage is input into the backstepping algorithm for voltage tracking. Finally, the algorithm tracks the reference voltage and makes the photovoltaic array operate at the maximum power point. The simulation is carried out by using MATLAB/Simulink. The IP&O-backstepping algorithm is compared with the intelligent algorithm and the traditional method, and the results show that compared to the above algorithm, the IP&O-backstepping algorithm can not only track the maximum power point of the photovoltaic array, but also has a faster tracking speed, and the output power has almost no oscillation when the photovoltaic array runs at the maximum power point.

MoMFormer: Mixture of modality transformer model for vegetation extraction under shadow conditions

Accurate estimation of fractional vegetation coverage (FVC) is essential for assessing the ecological environment and acquiring ecological information. However, under natural lighting conditions, shadows in vegetation scenes can easily lead to confusion between shadowed vegetation and shadowed soil, leading to misclassification and omission errors. This issue limits the precision of both vegetation extraction and FVC estimation. To address this challenge, this study introduces a novel deep learning model, the Mixture of Modality Transformer (MoMFormer), which is specifically designed to mitigate shadow interference in vegetation extraction. Our model uses the Swin-transformer V2 as a feature extractor, effectively capturing vegetation features from a dual-modality (regular-exposure RGB and high dynamic range HDR) dataset. A dynamic aggregation module (DAM) is integrated to adaptively blend the most relevant vegetation features. We selected several state-of-the-art (SOTA) methods and conducted extensive experiments using a self-annotated dataset featuring diverse vegetation–soil scenes and compare our model with several state-of-the-art methods. The results demonstrate that MoMFormer achieves an accuracy of 89.43 % on the HDR-RGB dual-modality dataset, with an FVC accuracy of 87.57 %, outperforming other algorithms and demonstrating high vegetation extraction accuracy and adaptability under natural lighting conditions. This research offers new insights into accurate vegetation information extraction in naturally lit environments with shadows, providing robust technical support for high-precision validation of vegetation coverage products and algorithms based on multimodal data. The code and datasets used in this study are publicly available at https://github.com/hhhxiaohe/MoMFormer.

Double-Exposure Algorithm: A Powerful Approach to Address the Accuracy Issues of Fractional Vegetation Extraction under Shadow Conditions

When recording the vegetation distribution with a camera, shadows can form due to factors like camera angle and direct sunlight. These shadows result in the loss of pixel information and texture details, significantly reducing the accuracy of fractional vegetation coverage (FVC) extraction. To address this issue, this study proposes an efficient double-exposure algorithm. The method reconstructs the pixel information in shadow areas by fusing normal-exposure and overexposed images. This approach overcomes the limitations of the camera’s dynamic range in capturing pixel information in shadowed regions. The study evaluates images with five levels of overexposure combined with five vegetation extraction indices. The aim is to determine the best-performing double-exposure combination under shadow conditions and the most suitable vegetation index. Experimental results reveal that the R² value between the best vegetation index and the FVC calculated from the fused double-exposure images and the ground truth FVC increases from 0.750 to 0.969. The root mean square error (RMSE) reduces from 0.146 to 0.046, and the intersection over union (IOU) increases from 0.856 to 0.943. These results demonstrate the excellent vegetation extraction capability of the double-exposure algorithm under shadow conditions, offering a straightforward and effective solution to low accuracy of FVC in shadowed areas.

A novel algorithm MPPT controller based on the herd horse optimization for photovoltaic systems under partial shadow conditions

In recent years, a significant scientific issue has been the creation of maximum power point tracking (MPPT) methods to increase the energy production of PV plants. Moreover, to try to cope with the unparalleled operating conditions of PV plants, many bio-inspired meta-heuristic algorithms have already been suggested in the literature, but their implementation is often complex and difficult. In this sense, we propose a novel algorithm for monitoring the (MPPT), using the newly meta-heuristic approach of herd horse optimization (HHO). A DC/DC boost converter is utilised in the suggested controllers to extract the most power possible from the PV resource. The system is programmed and modelled using the MATLAB/SIMULINK software, which also studies four shadow models and a 3S1P topography of single-junction solar arrays. Considering partial shading conditions (PSC), the success of the power values in the global maximum power point (GMPP) of the proposed method is between 99.64% and 99.07%. Besides the time to capture the GMPP by the proposed algorithm is between 0.396 s and 1.666 s, shorter than that of the CSA and FPA algorithms. Comparison with the CSA and FPA optimizers confirmed the quality of the MPPT-based HHO algorithm for GMPP extraction in different (PSC).

A Study on An MPPT Control Approach Using Artificial Intelligence and the Perturb and Observe Method

A maximum power point tracking (MPPT) control procedure constructed based on the Artificial intelligence optimization algorithm is proposed. A mathematical model of photovoltaic cells was established under varying light intensity and ambient temperature. Maximal power tracking control for iterative search using an artificial intelligence (AI) optimization technique Excellent, the artificial intelligence-based MPPT algorithm's principle is presented. Simulation results it shows that the artificial intelligence algorithm can faster and exactly track the maximum power point and remains stable, and compared to Perturb and Observe algorithm under dynamic shadow conditions and artificial intelligence MPPT control method, which has higher tracking accuracy and faster convergence speed. Faster and smaller oscillation amplitude, which is the best response to sunlight conditions for photovoltaic maximum power point tracking technology Flexibility. Using Matlab/Simulink software to build a simulation model of an independent photovoltaic system. It controls variables by keeping the temperature continuous and changes the light intensity to simulate different lighting environments. The identical comparison was conducted between all based MPPT methods such as the fuzzy control approach, demonstrating that the fuzzy control based technique exhibits higher results in terms of efficiency.

Segmentation and deep learning to digitalize the kinematics of flow-type landslides

Flow-type landslides, including subaerial and submarine debris flows, have poor spatiotemporal predictability. Therefore, researchers rely heavily on experimental evidence in revealing complex flow mechanisms and evaluating theoretical models. To measure the velocity field of experimental flows, conventional image analysis tools for measuring soil deformation and hydraulics have been borrowed. However, these tools were not developed for capturing the kinematics of fast-moving soil–water mixtures over complex terrain under non-uniform lighting conditions. In this study, a new framework based on deep learning was used to automatically digitalize the kinematics of experimental flow-type landslides. Captured images were broken into sequences and binarized using a fully convolutional neural network (FCNN). The proposed framework was demonstrated to outperform classic image processing algorithms (e.g., particle image velocimetry, trainable Weka segmentation, and thresholding algorithms) over a wide range of experimental conditions. The FCNN model was even able to process images from consumer-grade cameras under complex shadow, light, and boundary conditions. This feature is most useful for field-scale experimentation. With fewer than 15 annotated training images, the FCNN digitalized experimental flows with an accuracy of 97% in semantic segmentation.

Impact of temperature and solar irradiance in shadow covering scenarios via two-way sensitivity analysis for rooftop solar photovoltaics

ABSTRACT Solar irradiance and temperature are two primary factors that affect the energy generation efficiency of solar photovoltaic (PV) systems, meaning that climate change may significantly impact the production of solar energy in the future. In this study, a two-way sensitivity analysis is carried out to determine the energy generation potential under future climate change conditions, and conditions of shadow covering are also considered. The simulation results indicate that the impact of solar irradiance causes the most variation in energy generation, and that shadow covering has a weak impact on the output. In the one-way sensitivity analysis, solar irradiance makes a ± 18% difference in energy generation, whereas shadow covering represents about ± 2%. In the future trend of climate change in the world and Taiwan, the energy generation potential can decrease up to 21% and increase 8%, respectively, mainly corresponded by solar irradiance. We suggest that compared with the issue of temperature, the efficiency of solar irradiance usage is more important, especially in view of the global trend toward solar irradiance loss. Taiwan also suffers the issue, but the different trend of climate change impact may make the challenge different.

A Contribution to Isolated and Grid-Connected Photovoltaic Systems under Shadow Conditions

The power supplied by photovoltaic panel is sensitive to radiation incident on itself. Both isolated and grid connected applications are very important output voltage. The load usually works in constant voltage. Variation on radiation levels may lead to no voltage. This work presents the use of a PID controller in a converter able to provide constant voltage for an isolated system even during radiation variation in solar panel. presents a PI controller to a three-phase inverter connected system. The controller is designed power produced by the photovoltaic panels on the grid.

High-Resolution Flood Monitoring Based on Advanced Statistical Modeling of Sentinel-1 Multi-Temporal Stacks

High-resolution flood monitoring can be achieved relying on multi-temporal analysis of remote sensing SAR data, through the implementation of semi-automated systems. Exploiting a Bayesian inference framework, conditioned probabilities can be estimated for the presence of floodwater at each image location and each acquisition date. We developed a procedure for efficient monitoring of floodwaters from SAR data cubes, which adopts a statistical modelling framework for SAR backscatter time series over normally unflooded areas based on Gaussian processes (GPs), in order to highlight flood events as outliers, causing abrupt variations in the trends. We found that non-parametric time series modelling improves the performances of Bayesian probabilistic inference with respect to state-of-the-art methodologies using, e.g., parametric fits based on periodic functions, by both reducing false detections and increasing true positives. Our approach also exploits ancillary data derived from a digital elevation model, including slopes, normalized heights above nearest drainage (HAND), and SAR imaging parameters such as shadow and layover conditions. It is here tested over an area that includes the so-called Metaponto Coastal Plain (MCP), in the Basilicata region (southern Italy), which is recurrently subject to floods. We illustrate the ability of our system to detect known (although not ground-truthed) and smaller, undocumented inundation events over large areas, and propose some consideration about its prospective use for contexts affected by similar events, over various land cover scenarios and climatic settings.

Optimasi PV Array Menggunkan Maximum Power Point Tracking dengan Algoritma FireFly dan Partical Swarm Optimization kondisi Normal dan Partial Shadding

Most solar power plants are not capable of operating independently at maximum capacity, the voltage characteristics of solar energy usually follow the voltage (V) of the storage (battery) or load (load) which is directly connected to solar energy. Of course, the irradiation received by the PV module does not always receive uniform radiation so that the power produced does not always have maximum power and causes double peaks. An MPPT (Maximum Power Point Tracking) system is required to optimize PV output. However, the commonly used methods are often trapped by peaks below the optimal peak and require a long convergence time. This research compares the tracking performance and tracking time of two methods, FF (Firefly Algorithm) and PSO (Particle Swarm Optimization), to find the best performance in partial shadow conditions. The PSO algorithm has an average efficiency of 99.1043% and the FF algorithm has an average efficiency of 99.4073% in 10 trials in each of the 6 cases.

Multi-Scale Influence Analysis of Urban Shadow and Spatial Form Features on Urban Thermal Environment

In urban thermal environment research (UTE), urban shadows formed by buildings and trees contribute to significant variations in thermal conditions, particularly during the mid-day period. This study investigated the multi-scale effects of indicators, including urban shadows, on UTE, focusing specifically on the mid-day hours. It integrated field temperature measurements and drone aerial data from multiple city blocks. Considering both urban shadows and direct solar radiation, a linear mixed-effects model was employed to study the multi-scale effects of urban morphological indicators. Results showed that: (1) UTE is a multi-scale, multi-factor phenomenon, with thermal effects manifesting at specific scales. Under shadow conditions, smaller scales (10–20 m) of landscape heterogeneity and larger scales (300–400 m) of landscape consistency better explained temperature variations mid-day. Conversely, under direct sunlight, temperature was primarily influenced by larger scales (150–300 m). (2) Trees significantly reduced temperature; 100% tree canopy cover within a 10-m radius reduced air temperatures by approximately 2 °C mid-day. However, there is no significant correlation between temperature and green spaces. (3) Building area and height were significantly correlated with temperature. Specifically, an increase in building area beyond 150 m, especially within a 300-m radius, leads to higher temperatures. Conversely, building height within a 10–20 m range exhibits significant cooling effects. These findings provide crucial reference data for micro-scale UTE investigations during mid-day hours and offer new strategies for urban planning and design.

SiamC Transformer: Siamese coupling swin transformer Multi-Scale semantic segmentation network for vegetation extraction under shadow conditions

The accuracy of fractional vegetation cover (FVC) estimation has important significance in high-precision agricultural and ecological environment assessments. However, the presence ofshadowsunder natural light conditions can cause problems such as poor vegetation distinguishability and blurred boundaries during vegetation segmentation. This leads to missing or over-segmented vegetation inshadedareas, thus reducing the accuracy of FVC. Polarizationinformation can reflect the texture structure features, edgecharacteristics and surface state information, providing important supplementary information to light intensity information. To address the issue of shadows interfering with vegetation segmentation, a Siamese coupling swin transformer (SiamC Transformer) is proposed in this study. In this network, a dual-flow structure is used to simultaneously retrieve the feature information of vegetation degree of linear polarization (DoLP) images and light intensity images simultaneously to obtain multi-dimensional global semantic information of multi-scale vegetation. In the feature fusion stage, two fusion modules are proposed: an adaptive fusion module (AFM) and an adaptive fusion plus module (AFPM), which can fuse shallow spatial information, texture information and deep semantic information. The AFM and AFPM enable the network to better achieve object localization, region activation and edge sharpening, while improving the segmentation accuracy of small objects. The experimental results show that the mean intersection over union (mIoU) of this network is as high as 97.46% on vegetation datasets consisting of light intensity and polarization images, which is better than that of other algorithms. This network has higher accuracy and adaptability for shadow scenes and improves the accuracy of FVC calculation under shadow conditions.

Coverage Improvements for Sub-Terahertz Systems Under Shadowing Conditions

Radio propagation in the millimeter wave and sub-terahertz domain is heavily affected by shadowing conditions. The communication link is blocked without any additional technical means being used. Coverage improvements can be provided by using reflectors, RIS arrays, and repeaters to direct radio waves around corners or obstacles. These concepts show different performance and complexity levels affecting their network deployment. This paper investigates the achievable radio range or the received power to compare specific deployment concepts under realistic propagation conditions. Overall, the repeater solution provides either the largest radio range or the lowest necessary total transmit power compared to reflectors or RIS arrays and, thereby, is the most sustainable approach. A RIS array requires an additional centralized signal processing capacity for calculating optimized RIS settings and results in the highest level of network deployment complexity.

Intelligent Optimization Control Method for Photovoltaic Power Generation Systems Under Shadow Occlusion Conditions

<p>In the process of photovoltaic power generation, maximum power point tracking is an important method to improve the efficiency of photovoltaic power generation. Under the actual local shadow condition, the maximum power point of Photovoltaic system fluctuates. For this reason, this paper establishes the mathematical model and output characteristic equation of photovoltaic cells according to the actual application, and then uses the adaptive inertia weight Particle Swarm Optimization algorithm to solve the problem of slow search speed and low accuracy in the process of maximum power point tracking. After optimization, the method proposed in this paper can significantly improve the tracking effect efficiency, and the optimization results in real operation scenarios can improve the photovoltaic cell power generation efficiency by 21.3%, which proves the effectiveness of the algorithm.</p> <p> </p>

Experimental study of an optical concentrated solar thermoelectric power generator

The commercial thermoelectric modules become more and more efficient in terms of mechanical reliability, cooling capacity (in Peltier mode), power generation (in Seebeck mode), tolerable thermal conditions and so on. In this paper a portable optical concentrated solar thermoelectric generator is proposed, designed, tested, and evaluated based on a single UTX15-288-F2 module in both transient and steady-state conditions. A 30 cm diameter optical concentrator is employed to concentrate the solar power on one side of thermoelectric. Due to the low scale of the generated power, the active cooling methods for the other side of the module is not desirable and economical. That is why the boundary condition of the other low temperature side of thermoelectric is just natural convection by a heatsink under shadow condition. The aim of this case study is to clarify the UTX15-288-F2 module behavior under the mentioned conditions for possible portable micropower applications. Generated power, solar radiation, temperature of both side of the module and ambient conditions are measured during the experiments, reported, discussed, and analyzed.

Glide-snow avalanche characteristics at different timescales extracted from time-lapse photography

Abstract Glide-snow avalanches release due to a loss of friction at the snow–ground interface, which can result in large avalanches that endanger infrastructure in alpine regions. The underlying processes are still relatively poorly understood, in part due to the limited data available on glide processes. Here, we introduce a pixel-based algorithm to detect glide cracks in time-lapse photographs under changing illumination and shadow conditions. The algorithm was applied to 14 years of time-lapse images at Dorfberg (Davos, Switzerland). We analysed 947 glide-snow events at a high-spatial (0.5 m) and temporal (2–15 min) resolution. Avalanche activity and glide-crack opening dynamics were investigated across timescales ranging from seasonally to hourly. Events were separated into surface (meltwater percolation) and interface events (no meltwater percolation). The results show that glide activity is highly variable between and within seasons. Most avalanches released without crack formation or within 24 h after crack opening, and release was favoured in the afternoon hours. Glide rates often showed a stick–slip behaviour. The acceleration of glide rates and non-constant increases in glide crack aspect ratio were indicators for avalanche release. This comprehensive dataset provides the basis for further investigations into glide-snow avalanche drivers.

Chromosome number of <i>Carex brevispicula</i> (Cyperaceae), a sedge endemic to Korea

<i>Carex brevispicula</i> (Cyperaceae) is endemic to Korea and is characterized by constricted achenes, short lateral spikes, and awned staminate and pistillate scales. The species classified in sect. <i>Mitratae</i> occurs throughout South Korea, perennating on mountains and/or rocky slopes under half shadow conditions. Meiotic chromosomes of the species were examined in this study, in which 33 meiotic cells from seven populations were found to be less than 2 μm long with non-constricted chromosomes (<i>n</i> = 34<sub>II</sub>). The stable chromosome number may be related to the narrow geographical distribution and/or distinct achene morphology. Further investigations of the distribution, morphological character variation, and chromosome characteristics should be conducted with closely related taxa to understand the derivation of the species and its endemism in Korea.

Design of the particle swarm optimization simulated annealing MPPT control method based on motion shadow

Under the same environmental conditions, photovoltaic arrays at different operating voltages will significantly affect the size of solar power. To obtain as much power as possible, the maximum power point tracking (MPPT) control method has been invented. Previous studies focused on the tracking problem under local shadow conditions, but the dynamic shadow problem with rapid changes in local shadow was rarely discussed. To solve the tracking problem that the maximum power point has multi-peak and fast-changing characteristics under this condition, this paper proposes a simulated annealing MPPT control method based on particle swarm optimization, which compensates the disadvantages of the memoryless Markov chain in the optimization algorithm by adaptive asymmetric memory factor, thus improving the local search ability and convergence speed in the later stage. The control effect is verified by modeling and simulation in Simulink of MATLAB, and the adaptability of the heuristic control method is reflected by simulating the tracking mode under the moving shadow condition through the actual hardware deployment on the moving car.

A Novel Hybrid Maximum Power Point Tracking Technique for PV System under Complex Partial Shading Conditions in Campus Microgrid

Solar generation has become increasingly important in grid applications. In order to improve the energy efficiency of the photovoltaic array (PV), factors such as temperature, nonlinear characteristics, and partial shadow conditions (PSCs) of the PV must be fully considered. An excellent maximum power point tracking (MPPT) control strategy can effectively improve the energy utilization efficiency of photovoltaic cells and provide strong support for the construction of smart campuses in terms of environmental protection and energy saving. A traditional method such as Perturb & Observe (P&O) and incremental conductance (INC) will fall into the local maximum power point (LMPP). In the past decade, researchers have proposed many MPPT methods to solve the difficulties of the PV system. However, they have failed to fully consider dynamic changes in irradiance conditions. Changes in the irradiance of photovoltaic arrays can lead to an extension of the convergence time and an increase in the oscillation amplitude. Many current MPPT methods have shortcomings such as requiring a long convergence time, large oscillation amplitude, and being prone to falling into LMPP. In order to reduce the oscillation amplitude and improve the convergence speed, a novel Multi-strategy Improved Tuna Swarm Optimization hybrid INC (ITSO-INC) method is introduced in this article. This strategy involves improving the Tuna Swarm Optimization (TSO) through Levy Flight and a linear weight coefficient. In addition, the INC method is added in the later stage to improve the accuracy of MPPT tracking. The proposed algorithm can extract the global maximum power point under different partial shading. In order to verify the effectiveness of the proposed method, the proposed method was compared with other metaheuristic algorithms such as Cuckoo Search (CS) and TSO. The proposed ITSO-INC technique was tested over four different patterns of partial shading conditions. Modulation was performed by tracking the sudden change in the shadow pattern of the MPP. These simulation results confirm that the proposed method has fast convergence, high accuracy, zero steady state oscillation, and a rapid response to dynamic change.

Extracting vegetation information from high dynamic range images with shadows: A comparison between deep learning and threshold methods

Due to factors such as light angle and vegetation density, shadows can exist in ground vegetation images; this can lead to highly varying natural illumination in the images, which greatly limits the accuracy of fractional vegetation cover (FVC) estimation. While high dynamic range (HDR) images can reduce the extreme illumination contrast, the characteristic vegetation information in shadow areas is often lost. At present, most threshold methods do not fully utilize the spatial features of vegetation, and the results of vegetation classification under complex shadow conditions are associated with large errors. Thus, two deep learning methods (the HDR U-Net method and HDR U-Net++ method) are proposed in this study based on fully convolutional neural networks to extract vegetation information from HDR images under shadow conditions. These methods are compared with threshold methods. For full HDR images, the kappa coefficient and mean intersection over union (MIoU) of the HDR U-Net method and HDR U-Net++ method are 0.926, 0.931, and 0.874, 0.887, respectively. The FVC estimation accuracy and vegetation segmentation effect of these two deep learning methods are better than those of the three threshold methods considered. In addition, compared with normal exposure (NE) images, camera-based HDR images improve the extraction accuracy of deep learning methods and threshold methods for vegetation under shadow conditions. The results of this study suggest that deep learning methods are not affected by the threshold determination strategy selected and can more completely extract vegetation feature information from HDR images. The classification effect of vegetation under shadow conditions is effectively improved, and the vegetation segmentation and FVC estimation results are more precise. The combination of HDR images and deep learning methods can be applied to field scenes with complex lighting, which is beneficial to improve the ground data verification of remote sensing FVC products.