Index to 1984 NASA Tech Briefs, volume 9, numbers 1-4

Short announcements of new technology derived from the R&D activities of NASA are presented. These briefs emphasize information considered likely to be transferrable across industrial, regional, or disciplinary lines and are issued to encourage commercial application. This index for 1984 Tech B Briefs contains abstracts and four indexes: subject, personal author, originating center, and Tech Brief Number. The following areas are covered: electronic components and circuits, electronic systems, physical sciences, materials, life sciences, mechanics, machinery, fabrication technology, and mathematics and information sciences

The development of a resource-efficient photovoltaic system

This paper presents the measures taken in the demonstration of the photovoltaic case study developed within the European project ‘Towards zero waste in industrial networks’ (Zerowin), integrating the D4R (Design for recycling, repair, refurbishment and reuse) criteria at both system and industrial network level. The demonstration is divided into three phases. The first phase concerns the development of a D4R photovoltaic concept, the second phase focused on the development of a specific component of photovoltaic systems and the third phase was the demonstration of the D4R design in two complete photovoltaic systems (grid-connected and stand-alone). This paper includes a description of the installed photovoltaic systems, including a brief summary at component level of the lithium ion battery system and the D4R power conditioning system developed for the pilot installations. Additionally, industrial symbioses within the network associated with the photovoltaic systems and the production model for the network are described

How well does Learning-by-doing Explain Cost Reductions in a Carbon-free Energy Technology?

The incorporation of experience curves has enhanced the treatment of technological change in models used to evaluate the cost of climate and energy policies. However, the set of activities that experience curves are assumed to capture is much broader than the set that can be characterized by learning-by-doing, the primary connection between experience curves and economic theory. How accurately do experience curves describe observed technological change? This study examines the case of photovoltaics (PV), a potentially important climate stabilization technology with robust technology dynamics. Empirical data are assembled to populate a simple engineering-based model identifying the most important factors affecting the cost of PV over the past three decades. The results indicate that learning from experience only weakly explains change in the most important cost-reducing factors— plant size, module efficiency, and the cost of silicon. They point to other explanatory variables to include in future models. Future work might also evaluate the potential for efficiency gains from policies that rely less on ‘riding down the learning curve’ and more on creating incentives for firms to make investments in the types of cost-reducing activities quantified in this study.Learning-by-doing, Experience Curves, Learning Curves, Climate Policy

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

A Survey on Ageing Mechanisms in II and III-Generation PV Modules: Accurate Matrix-Method Based Energy Prediction Through Short-Term Performance Measures

none5siSolar energy utilization has been triggered by advances in new technology to reduce the cost of photovoltaic (PV) panels with an increase of efficiency. To improve the energy production quality, it is necessary to undergo the PV panels to characterization both in the indoor and outdoor scenarios; these latter characterizations generally require all seasons-based measurements. Therefore, it is essential to find models for characterizing PV panels in terms of energy production but also production and operating mode tolerance. The paper illustrates the findings of global research dedicated to PV panels ageing and their impact on energy production in the years. At first, an in-depth analysis of the ageing mechanisms affecting II and III generations' PV panels has been presented when exposed to atmospheric agents. Afterwards, the PV panels' characterization, conducted in a short time (i.e. a total of seven days), has been reported, performing outdoor measurements in conjunction with an electronic calibrator able to measure currents and voltages. The MPPT (Maximum Power Point Tracker) device is the core instrumentation of the employed measurement system. Obtained results are convincing since they have been compared with simultaneous measurements of PV panels located in the same place.openP. Visconti, R. de Fazio, D. Cafagna, R. Velazquez, A. Lay-EkuakilleVisconti, P.; de Fazio, R.; Cafagna, D.; Velazquez, R.; Lay-Ekuakille, A

Solar cell degradation : the role of moisture ingress

Moisture ingress is one of the key fault mechanisms responsible for photovoltaic (PV) devices degradation. Moisture and moisture induced degradation (MID) products can attack the solar cell and the PV module components which can lead to solar cell degradation (e.g., microcracks), corrosion, optical degradation, potential induced degradation (PID), etc. These MID mechanisms have dire implications for the performance reliability of PV modules. Understanding the influence of moisture ingress on solar PV device’s degradation will boost the interest in investing in solar PV power installations globally, especially in the Nordics. In this thesis, the effect of moisture ingress on 20-years old field-aged multicrystalline silicon (mc-Si) PV modules is investigated. The defective areas in the PV modules were identified using visual inspection, electroluminescence (EL), ultraviolet fluorescence (UV-F), and infrared thermal (IR-T) techniques. Scanning electron microscopy and energy dispersive Xray spectroscopy (SEM-EDS) analyses were used to elucidate the role of moisture on the observed degradation mechanisms. In addition, temperature coefficient profiling is used as a diagnostic tool to characterize different moisture induced defects. The ethylene vinyl acetate (EVA) front encapsulation was found to undergo optical degradation and the extracted cells show dark discolored Tedlar®/Polyester/Tedlar® (TPT) backsheets. Corrosion at the solder joint was dominant and is attributed to the dissolution of lead and tin (main components of solder) and the Ag grids in moisture and acetic acid due to galvanic corrosion. Degradation of the EVA encapsulation produces acetic acid, carbon dioxide, phosphorus, sulfur, fluorine, and chlorine. It was observed that under the influence of moisture ingress, leached metal ions e.g., Na, Ag, Pb, Sn, Cu, Zn, and Al migrate to the surface of the solar cells. This led to the formation of oxides, hydroxides, sulfides, phosphates, acetates, and carbonates of silver, lead, tin, copper, zinc, and aluminum. Also, other competing reactions led to the formation of stannates of copper, silver, sodium, and zinc. Similarly, migration of silver and aluminum to the surfaces of the TiO2 antireflection coating (ARC) nanoparticles (NPs) lead to the formation of titania-alumina and silver-titania complexes. Formation of these titania-metal complexes affects the opto-electrical efficiency of the TiO2 ARC in the PV module. Additionally, in the presence of moisture and acetic acid, Pb is preferentially corroded (to form lead acetate complexes) instead of the expected sacrificial Sn in the solder. In the EL and UV-F images, these degradation species appear as dark spots, and as hot spots in IR-T images. More importantly, these MID defects and fault modes lead to parasitic resistance and mismatch losses, and hence, degradation in the current-voltage (I-V) characteristics, temperature coefficients, and maximum power (Pmax) of the field-aged PV modules. The observed temperature sensitivities are characteristic of different moisture-induced defects. Taken together, this work has expounded on the understanding and detection of MID phenomenon in field-deployed solar PV modules.publishedVersio

An investigation of solar panel thermal images collected from an unmanned aerial vehicle

As the population of the world continues to increase, so does energy consumption. At the same time, available fossil fuels continue to be depleted. Knowing these two facts, there is a need to find additional sources of energy. Photovoltaic panels (solar panels) are front and center of the renewable energy available options. Exploring the practical use of infrared thermal imaging for data collection and maintenance of photovoltaic panels is the main objective of this study. In this research, three unmanned aerial vehicle (UAV) flights were completed to obtain thermal imaging of the Cedar Falls Utilities Solar Field with various dates and weather. The images obtained by the UAV show varying temperatures of solar panels. The comparison between the power output of the solar garden and the temperature of the panels themselves, did not show any significant correlation. The research opened up more questions and shows the need for more research on the topic of how to utilize drone and thermography technology to assist utility companies

Performance evaluation of the photovoltaic system

The various renewable energy source technologies, Photovoltaics (PV) transforming sunlight directly into electricity, have become standard practice worldwide, especially in countries with high solar radiation levels. PV systems have been developed rapidly over recent years, and many new technologies have emerged from different producers. For each type of PV module, manufacturers provide specific information on rated performance parameters, including power at maximum power point (MPP), efficiency and temperature factors, all under standard solar test conditions (STC) 1000 W/m2. Air. In addition, the mass (AM) of 1.5 and the cell's temperature was 25 ̊C. Unfortunately, this grouping of environmental conditions is infrequently found in outdoor conditions. Also, the data provided by the manufacturers are not sufficient to accurately predict the performance of photovoltaic systems in various climatic conditions. Therefore, monitoring and evaluating the performance of the off-site systems is necessary. This thesis aims to overview various photovoltaic technologies, ranging from crystalline silicon (c-SI) to thin-film CdTe and GiCs. The following are the main parameters for evaluating the external units' performance to describe the PV systems' operation and implementation. In addition, a review of the impacts of various environmental and operational factors, such as solar radiation, temperature, spectrum, and degradation