A Nonrelational Data Warehouse for the Analysis of Field and Laboratory Data From Multiple Heterogeneous Photovoltaic Test Sites

Hu, Yang; Gunapati, Venkat Yashwanth; Zhao, Pei; Gordon, Devin A.; Wheeler, Nicholas R.; Hossain, Mohammad A.; Peshek, Timothy J.; Bruckman, Laura S.; Zhang, Guoqiang; French, Roger H.

doi:10.1109/jphotov.2016.2626919

Cited by 40 publications

(23 citation statements)

References 22 publications

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“…The high-speed camera, above the spinning droplet ( Figure 3), collects in situ observations of the AlN formation and produces a time-series of gray-scale images for each droplet studied, amounting to 300 000 individual images which were stored in a distributed computing cluster based on Hadoop and Hbase for efficient data handling. 34 These images are processed to extract useful metrics of the solidification process. Image processing was performed using Python (v2.7) libraries, including Numpy, Scipy, Matplotlib, Pandas, Seaborn, Skimage, OpenCV and Trackpy.…”

Section: Image Processing and Analysismentioning

confidence: 99%

In‐situ observation of AlN formation from Ni‐Al solution using an electromagnetic levitation technique

et al. 2020

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Aluminum nitride is a promising substrate material for AlGaN-based UV-LED. In order to develop a robust growth processing route for AlN single crystals, fundamental studies of solution growth experiments using Ni-Al alloy melts as a new solution system were performed. Al can be stably kept in solution the Ni-Al liquid even at high temperature; in addition, the driving force of the AlN formation reaction from solution can be controlled by solution composition and temperature. To investigate AlN crystal growth behavior we developed an in situ observation system using an electromagnetic levitation technique. AlN formation behavior, including nucleation and growth, was quantitatively analyzed by an image processing pipeline. The nucleation rate of AlN decreased with increasing growth temperature and decreasing aluminum composition. In addition, hexagonal c-axis oriented AlN crystal successfully grew on the levitated Ni-40 mol%Al droplet reacted at low driving force (1960 K), on the other hand, AlN crystal with dendritic morphology appeared on the sample with higher driving force (Ni-50 mol%Al, 1960 K). Thus, the nucleation rate and crystal morphology were dominated by the driving force of the AlN formation reaction. K E Y W O R D S aluminum nitride, crystal growth, nucleation, thermodynamicsThis is an open access article under the terms of the Creat ive Commo ns Attri bution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. How to cite this article: Adachi M, Hamaya S, Yamagata Y, et al. In-situ observation of AlN formation from Ni-Al solution using an electromagnetic levitation technique. J Am Ceram Soc.

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Section: Image Processing and Analysismentioning

confidence: 99%

In‐situ observation of AlN formation from Ni‐Al solution using an electromagnetic levitation technique

et al. 2020

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“…29 Handling and analyzing the massive amount of time-series data from these sites is critical for understanding PV system degradation and lifetime performance (ie, the performance loss rate). Roger H. French shared recent data-driven R&D efforts for real-world big data generated from multiple heterogeneous photovoltaic (PV) test sites.…”

Section: Data Science: Informatics and Analyticsmentioning

confidence: 99%

“…29 In this manner we can present any company's results in comparison to the rest (the "bench"), and we can compare performance to climate zone, PV module or PV inverter brands and models, and to PV cell types and module materials. We de-identify and anonymize their systems, followed by data ingestion into our Hadoop cluster, and merge them into one comprehensive dataset.…”

Section: Data From Deployed Real-world Materials Systems Are a New Comentioning

confidence: 99%

Data‐driven glass/ceramic science research: Insights from the glass and ceramic and data science/informatics communities

Guire

Bartolo

Brindle³

et al. 2019

J Am Ceram Soc

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Data‐driven science and technology have helped achieve meaningful technological advancements in areas such as materials/drug discovery and health care, but efforts to apply high‐end data science algorithms to the areas of glass and ceramics are still limited. Many glass and ceramic researchers are interested in enhancing their work by using more data and data analytics to develop better functional materials more efficiently. Simultaneously, the data science community is looking for a way to access materials data resources to test and validate their advanced computational learning algorithms. To address this issue, The American Ceramic Society (ACerS) convened a Glass and Ceramic Data Science Workshop in February 2018, sponsored by the National Institute for Standards and Technology (NIST) Advanced Manufacturing Technologies (AMTech) program. The workshop brought together a select group of leaders in the data science, informatics, and glass and ceramics communities, ACerS, and Nexight Group to identify the greatest opportunities and mechanisms for facilitating increased collaboration and coordination between these communities. This article summarizes workshop discussions about the current challenges that limit interactions and collaboration between the glass and ceramic and data science communities, opportunities for a coordinated approach that leverages existing knowledge in both communities, and a clear path toward the enhanced use of data science technologies for functional glass and ceramic research and development.

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“…The understanding of degradation pathways, which is critical to establish the fundamental physics of PV degradation and promote reliability‐aware design, is still missing from these analyses. As a result, another online characterization approach—that can potentially identify degradation pathways from field data by machine learning algorithms—has gained attention: (d)Machine learning has been proved to be a potent tool to analyze massive data and generate useful insights for different applications. It can potentially provide valuable information on PV degradation by various statistical analyses (eg, regression, classification, clustering).…”

Section: Introductionmentioning

confidence: 99%

Real‐time monitoring and diagnosis of photovoltaic system degradation only using maximum power point—the Suns‐Vmp method

Sun

Chavali

Alam

2018

Progress in Photovoltaics

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The uncertainties associated with technology‐specific and geography‐specific degradation rates make it difficult to calculate the levelized cost of energy, and thus the economic viability of solar energy. In this regard, millions of fielded photovoltaic modules may serve as a global testbed, where we can interpret the routinely collected time series maximum power point (MPP) data to assess the time‐dependent “health” of solar modules. The existing characterization methods, however, cannot effectively mine/decode these datasets to identify various degradation pathways. In this paper, we propose a new methodology called the Suns‐Vmp method, which offers a simple yet powerful approach to monitoring and diagnosing time‐dependent degradation of solar modules by using the MPP data. The algorithm reconstructs “IV” curves by using the natural illumination‐dependent and temperature‐dependent daily MPP characteristics as constraints to fit physics‐based circuit models. These synthetic IV characteristics are then used to determine the time‐dependent evolution of circuit parameters (eg, series resistance), which in turn allows one to deduce the dominant degradation modes (eg, solder bond failure) of solar modules. The proposed method has been applied to a test facility at the National Renewable Energy Laboratory. Our analysis indicates that the solar modules degraded at a rate of ~0.7%/year because of discoloration and weakened solder bonds. These conclusions are validated by independent outdoor IV measurements and on‐site imaging characterization. Integrated with physics‐based degradation models or machine learning algorithms, the method can also serve to predict the lifetime of photovoltaic systems.

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A Nonrelational Data Warehouse for the Analysis of Field and Laboratory Data From Multiple Heterogeneous Photovoltaic Test Sites

Cited by 40 publications

References 22 publications

In‐situ observation of AlN formation from Ni‐Al solution using an electromagnetic levitation technique

In‐situ observation of AlN formation from Ni‐Al solution using an electromagnetic levitation technique

Data‐driven glass/ceramic science research: Insights from the glass and ceramic and data science/informatics communities

Real‐time monitoring and diagnosis of photovoltaic system degradation only using maximum power point—the Suns‐Vmp method

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