Perbandingan Perolehan Daya Solar Panel Monocrystalline Terhadap Solar Panel Polycrystalline

The availability of two types of solar panels that are common in the market namely monocrystalline and polycrystalline types cause confusion in the selection so that many solar panel users are questioning the differences of these two types of solar panel. This study produced a data logger system using Arduino Uno R3 to control voltage, current and temperature sensors for logging data that stores power measurement data from monocrystalline and polycrystalline solar panel in a micro SD. After it we can manage data to compare power produced between two types the solar panel. From the results of testing this data logger system it can be seen that monocrystalline solar panel are 9.18% better on power produced than polycrystalline when the maximum power conversion is generated

Floating solar panel park

Treball desenvolupat dins el marc del programa 'European Project Semester'.This Final Report is the culmination of a four month long design study on floating solar panel park feasibility in Vaasa, Finland. The Floating Ideas Team was tasked with coming up with a design that would not only work, but also make a profit. The team focused a lot of time on initial research, an iterative design process, and experiments to gather information that could not be found during the research phase. In this report, one can expect to find the major findings from research in many different areas such as location, panel design, flotation design, cooling techniques, and efficiency adding techniques. The first takeaway is that implementing floating solar parks in Finland would require adding efficiency techniques such as mirrors or concentrators. Second, how the panels are placed means a lot in a location so far north. Placing the panels far away from each other and horizontally will reduce the negative impact of shadows. And third, the rotation of the structure is important in increasing efficiency. Multiple axis tracking is not necessary, but tracking in the vertical axis can add a 50% increase in power generated. This research then lead into the defining of four initial designs which were eventually paired down into one. The largest factors leading to the change in design were the combination of rotation and anchoring methods, the flotation structure, and the structure required hold the panel modules together. In the end, the final design is a modular circular design with panels and mirrors to help add efficiency, approximately 37%. From there, an economic and environmental feasibility study was done and for both, this design was deemed feasible for Finland. With the design, detailed in this report, it would be possible to implement this and make a profit off of it, leading the team to believe that this should be implemented in places looking for alternatives for renewable energy production

Solar panel fabrication Patent

Method and apparatus for fabricating solar cell panel

POWER TRANSISTOR AND PHOTODIODE AS A SOLAR CELL DEVICE

Novel solar panel using BPW41N Photodiode have been developed. The panel produced current of 714μA at 6.17V and 375μA at 8.60V using type A and B respectively. The combination of type A and B produced current of 395μA at 13.80V which is a 5.45mW solar panel

Automated solar panel assembly line. LSA task; production processes and equipment

An automated solar panel production line which reduces the module assembly costs was designed. The module design, solar cell assembly phototype, and solar panel lamination prototype are discussed

Lightweight solar panel development

Preliminary design, fabrication, and test of lightweight solar panel of built-up beryllium structure with 29 sq ft active cell are

Eternal Sunshine of the Solar Panel

The social dynamics of residential solar panel use within a theoretical population are studied using a compartmental model. In this study we consider three solar power options commonly available to consumers: the community block, leasing, and buying. In particular we are interested in studying how social influence affects the dynamics within these compartments. As a result of this research a threshold value is determined, beyond which solar panels persist in the population. In addition, as is standard in this type of study, we perform equilibrium analysis, as well as uncertainty and sensitivity analyses on the threshold value. We also perform uncertainty analysis on the population levels of each compartment. The analysis shows that social influence plays an important role in the adoption of residential solar panels

Mariner Venus 67 solar panel

Design, assembly, tests, and postflight analysis of Mariner Venus 67 solar pane

DeepSolarEye: Power Loss Prediction and Weakly Supervised Soiling Localization via Fully Convolutional Networks for Solar Panels

The impact of soiling on solar panels is an important and well-studied problem in renewable energy sector. In this paper, we present the first convolutional neural network (CNN) based approach for solar panel soiling and defect analysis. Our approach takes an RGB image of solar panel and environmental factors as inputs to predict power loss, soiling localization, and soiling type. In computer vision, localization is a complex task which typically requires manually labeled training data such as bounding boxes or segmentation masks. Our proposed approach consists of specialized four stages which completely avoids localization ground truth and only needs panel images with power loss labels for training. The region of impact area obtained from the predicted localization masks are classified into soiling types using the webly supervised learning. For improving localization capabilities of CNNs, we introduce a novel bi-directional input-aware fusion (BiDIAF) block that reinforces the input at different levels of CNN to learn input-specific feature maps. Our empirical study shows that BiDIAF improves the power loss prediction accuracy by about 3% and localization accuracy by about 4%. Our end-to-end model yields further improvement of about 24% on localization when learned in a weakly supervised manner. Our approach is generalizable and showed promising results on web crawled solar panel images. Our system has a frame rate of 22 fps (including all steps) on a NVIDIA TitanX GPU. Additionally, we collected first of it's kind dataset for solar panel image analysis consisting 45,000+ images.Comment: Accepted for publication at WACV 201