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Core Objective 1: Highlights from
the Central Data Resource
Anubhav Jain w/
Robert White, Todd Karin, Mike
Woodhouse, & Cl...
The Central Data Resource develops and disseminates
solar-related data, tools, and software
DuraMat projects
generate data...
Central Data Resource (Data Hub) objectives
DuraMat
Data Hub
Data Software
Analysis
Tools
• Objective: Collect and dissemi...
“A central data resource that securely
hosts a mix of private and public data of
multiple data types”
The Data Hub at a glance
137 Users
63 Projects
128 Datasets
70 Public Datasets
2120 Files and Resources
Google analytics J...
How data is organized
Project Dataset Files or Resources
CSV
PDF
TEXT
XML
JSON
JPG
GIF
PNG
Excel
Team Member Access:
All p...
Highlights from current data sets
1. Albedo Data for Bifacial Systems
2. Coatings to Reduce Soiling and PID Losses
3. NREL...
Highlights from current data sets
1. Albedo Data for Bifacial Systems
2. Coatings to Reduce Soiling and PID Losses
3. NREL...
“Development of open-source software
libraries that apply data analytics to solve
module reliability challenges”
Technology Summary and Impact
Resources
Automatic Crack Detection using Convolutional Neural Networks
• Take in electrolum...
Technology Summary and Impact
Resources
Detecting changes in module parameters using production data sets
• Goal: Use oper...
Technology Summary and Impact
Resources
A Quick and Easy to Use LCOE calculator
• Provide a visual, user-friendly tool for...
• Software and algorithms for data cleaning
– https://github.com/pvlib/pvanalytics [[POSTER: CO1-1, Hansen]]
• PV-Terms - ...
• The Central Data Resource aims to
maximize the potential of applying
data as a resource to help solve
problems related t...
Q&A
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Core Objective 1: Highlights from the Central Data Resource Slide 1 Core Objective 1: Highlights from the Central Data Resource Slide 2 Core Objective 1: Highlights from the Central Data Resource Slide 3 Core Objective 1: Highlights from the Central Data Resource Slide 4 Core Objective 1: Highlights from the Central Data Resource Slide 5 Core Objective 1: Highlights from the Central Data Resource Slide 6 Core Objective 1: Highlights from the Central Data Resource Slide 7 Core Objective 1: Highlights from the Central Data Resource Slide 8 Core Objective 1: Highlights from the Central Data Resource Slide 9 Core Objective 1: Highlights from the Central Data Resource Slide 10 Core Objective 1: Highlights from the Central Data Resource Slide 11 Core Objective 1: Highlights from the Central Data Resource Slide 12 Core Objective 1: Highlights from the Central Data Resource Slide 13 Core Objective 1: Highlights from the Central Data Resource Slide 14 Core Objective 1: Highlights from the Central Data Resource Slide 15
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Core Objective 1: Highlights from the Central Data Resource

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Presentation given at the virtual Durable Module Materials (DuraMat) workshop, Sept 2020

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Core Objective 1: Highlights from the Central Data Resource

  1. 1. Core Objective 1: Highlights from the Central Data Resource Anubhav Jain w/ Robert White, Todd Karin, Mike Woodhouse, & Cliff Hansen DuraMat Fall Workshop, Sept 21 2020
  2. 2. The Central Data Resource develops and disseminates solar-related data, tools, and software DuraMat projects generate data sets Data enters the DataHub and is access-controlled by project Users access data sets, visual analysis tools, and open-source software
  3. 3. Central Data Resource (Data Hub) objectives DuraMat Data Hub Data Software Analysis Tools • Objective: Collect and disseminate module reliability related data, and apply data science to derive new insights from data • Key results include: – A central data resource that securely hosts a mix of private and public data of multiple data types – Development of open-source software libraries that apply data analytics to solve module reliability challenges – Demonstrated use case of above tools to an industrial use case
  4. 4. “A central data resource that securely hosts a mix of private and public data of multiple data types”
  5. 5. The Data Hub at a glance 137 Users 63 Projects 128 Datasets 70 Public Datasets 2120 Files and Resources Google analytics July 1 –Aug 10, 2020: https://datahub.duramat.org
  6. 6. How data is organized Project Dataset Files or Resources CSV PDF TEXT XML JSON JPG GIF PNG Excel Team Member Access: All project information and contained datasets and files Public Access: Project names, descriptions and abstracts + Links to external data 63 available 128 available (70 public) 2120 available Contact: Robert White Data is PRIVATE by default Data can be uploaded by DuraMat project members and vetted sources Data can be accessed by registering on the Data Hub web site Data can be made public through administrative control, with authorization
  7. 7. Highlights from current data sets 1. Albedo Data for Bifacial Systems 2. Coatings to Reduce Soiling and PID Losses 3. NREL Soiling map and supporting data 4. Bifacial Experimental Single-Axis Tracking Field data For the public: 1. Back side defect imaging in crystalline silicon PV modules 2. Identifying Degradation Mechanisms in Fielded Modules using Luminescence and Thermal Imaging 3. Combined Accelerated Stress Testing 4. Effect of Cell Cracks on Module Power Loss and Degradation For project members
  8. 8. Highlights from current data sets 1. Albedo Data for Bifacial Systems 2. Coatings to Reduce Soiling and PID Losses 3. NREL Soiling map and supporting data 4. Bifacial Experimental Single-Axis Tracking Field data For the public: 1. Back side defect imaging in crystalline silicon PV modules 2. Identifying Degradation Mechanisms in Fielded Modules using Luminescence and Thermal Imaging 3. Combined Accelerated Stress Testing 4. Effect of Cell Cracks on Module Power Loss and Degradation For project members See also: Poster C01 -2 from Robert White
  9. 9. “Development of open-source software libraries that apply data analytics to solve module reliability challenges”
  10. 10. Technology Summary and Impact Resources Automatic Crack Detection using Convolutional Neural Networks • Take in electroluminescence images of full modules, automatically crop out cells, and identify cracks and power loss regions • Working with EPRI to correlate cracks with power loss • Testing on diverse images with PVEL • https://github.com/hackingmaterials/pv-vision • Poster presentation by Cara Libby in CO3-3: “Effect of Cell Cracks on Module Power Loss & Degradation” Cracks, defects, and other features predicted by U-Net machine learning model Busbar detection (gold) Cracks (purple) Power loss regions (green)Cells in module are automatically detected, cropped out, and perspective corrected Contact: Xin Chen
  11. 11. Technology Summary and Impact Resources Detecting changes in module parameters using production data sets • Goal: Use operating / production data (e.g., Vmp and Imp, and Tcell) to determine changes in module parameters (e.g., Rseries and Rshunt) over time • Method based on “Suns-Vmp”1 • Compare method with systems for which detailed diagnostics are available, e.g., NREL SERF East • https://github.com/DuraMat/pvpro [[in development]] • Poster presentation by Todd Karin in CO1-4: “Introduction to PVPRO” • Next talk in this workshop By analyzing changed in Vmp and Imp throughout the course of deployment, PV-PRO aims to detect changes in module parameters Contact: Todd Karin arrays. But about a fifth of the observed changes were from the inverter not tracking the peak-power as effectively as the PV arrays aged. 1. Background As part of the construction of the Solar Energy Research Facility building at the National Renewable Energy Laboratory in Golden, Colorado, two grid-connected photovoltaic systems were installed on the roof to provide power to the building and the utility grid. Corresponding to their location on the building, the systems are identified as SERFEAST and SERFWEST. The SERFEAST PV array is shown in Fig. 1. Figure 1. SERFEAST array on the roof of the building. Each PV array consists of 140 Siemens Solar Industries model M55 PV modules. The PV arrays are electrically connected as five source-circuits, with each source-circuit having a positive and negative monopole of 14 series- connected PV modules. Each PV array is connected to an 8 kW Omnion Series 2200 inverter for conversion from d.c. to a.c. power. The PV arrays are tilted from the horizontal at an angle of 45 and are aligned with the azimuth of the building that is oriented 22 east of south. The longitude and degradation over the 8-year period. 2. Data Screening For calculating PV system ratings, data were selected to meet meteorological criteria and to avoid data recorded when the inverters were malfunctioning or off-line for repairs. Meteorological criteria for data selection were a 15- minute average POA irradiance greater than 800 W/m2 and an angle-of-incidence of direct-beam radiation to the PV array of less than 30 degrees. This ensures that the cloud presence was small and that the pyranometer measurement of irradiance was performed within a range of incident angles where the cosine response of the pyranometer is not detrimental to measurement accuracy. A region of acceptable PV array operating voltages as a function of PV array temperature was identified using data recorded during normal system operation. This resulted in the “boxed” area shown in Fig. 2. Data within the “boxed” area were judged acceptable for use for data analysis, whereas data in the remaining area were judged unacceptable because they were measured under malfunctioning or system-off (open-circuit) conditions. Normal operation for these systems does not necessarily mean peak-power tracking, although that was the original intent. The inverters were specially ordered to achieve a peak-power tracking range of 200 to 280 volts. However, as delivered, the inverters do not operate below about 220 volts. Consequently, for elevated PV array temperatures, the inverters do not peak-power track because the PV arrays are operated at 220 volts and the PV array voltage for maximum power (Vmp) is considerably less. The diagonal lines in Fig. 2 represent PV array Vmp values as a function of PV array temperature for 1994 and 2002. They were determined from PV module and array current- voltage (I-V) curve measurements. Values of Vmp for 2002 are about 10 volts less than they were in 1994; consequently, for elevated PV array temperatures in 2002, the inverter operates the PV array further from its peak- power point than in 1994. As an example, the power penalty for not peak-power tracking at a PV array temperature of 1 Fig. 6. Pm ax , Isc , Voc , and FF degradation for all measured strings and arrays. The East array is listed on top and the West array at the bottom. The string polarity is indicated by negative (N) and positive (P). and not voltage. The fairly large uncertainties are caused by the multiple data shifts that needed to be corrected. IV. OUTDOOR I–V MEASUREMENTS A total of eight sets of I–V measurements were taken dur- ing the 20-year lifetime of the system. The measured module temperatures were translated to 45 °C, which presented a good approximation to the average temperature for this particular lo- cation, and irradiance to 1000 W/m2 . It was not clear whether a linear or nonlinear regression resulted in a better fit to the data. Thus, in the absence of a clear indication, a linear regression line was used through the eight data points for each string, subarray, and array [8], [9]. The resulting degradation for each parameter, subarray, and string are summarized in Fig. 6. Maximum power (Pmax) is indicated by blue circles, short-circuit current (Isc) by red squares, open-circuit voltage (Voc) by green triangles, and fill factor (FF) by inverted purple triangles. The strings for the East array are shown on top and those for the West array at the bottom. The uncertainty bars are statistical uncertainties cal- culated from the regression standard errors. For the East array (top), the Pmax degradation for the strings of negative polarity is between 0.4%/year and 0.6%/year with the exception of string 3. String 3 shows a higher degradation rate of about 0.8%/year that seems to determine the overall degradation of the negative sub- array. The decline appears to be dominated by FF decline for this particular string, which is typically associated with reduced shunt resistance or increased series resistance [10]. Increased series resistance for aged PV systems is often caused by flawed solder interconnects in combination with thermal cycling and manifests itself by localized hot spots [11]. Less of the decline can be attributed to Isc degradation, which is typically associ- ated with light-induced degradation, discoloration, delamina- tion, and soiling [12], [13]. Fig. 7 shows optical and infrared (IR) images of observed discoloration, soiling, and local hotspots, visually corroborating the I–V analysis. Nevertheless, the discoloration appears to be less than in hotter climates for similar modules [14]. The overall Fig. 7. Optical images of the system show some discoloration in the center of most cells (a), permanent soiling (b), and some hotspots in IR imaging (c). Photos (a), (b), and (c) by D. Jordan, NREL. subarray degradation of about 0.7%/year is slightly less than the average published literature Pmax degradation of 0.8%/year [3]. Historical degradation is more dominated by the Isc decline of about 0.5%/year and 0.3%/year of FF, while the decline for this particular system is more mixed or even more FF attributable. Similarly to historical degradation, Voc degrades the least. For the positive polarity, the Pmax degradation is more evenly spread between the individual strings leading to a degradation of the subarray of about 0.7%/year. Strings 3–5 are dominated by FF losses, while strings 1 and 2 are characterized by more dominant Isc losses. For the West array with negative polar- ity, most strings degrade in Pmax in the 0.5–0.6%/year range. Only string 2 degrades in the 0.7%/year range, thus apparently determining the overall degradation for the subarray. Strings 2 and 3 show an equal degradation that is attributable to Isc and FF decline. The other strings show more dominating FF losses. The positive polarity of the West array shows the overall highest Pmax degradation, which seems to be significantly influenced by string 5. That is also the string that shows significant Voc losses compared with all other strings. Hotspots / Rs increase over time 1. Sun, X., Chavali, R. V. K. & Alam, M. A. Prog Photovolt Res Appl 27, 55–66 (2019).
  12. 12. Technology Summary and Impact Resources A Quick and Easy to Use LCOE calculator • Provide a visual, user-friendly tool for quick “back-of-the-envelope” of LCOE • Make rough estimates such as “if I deploy a coating that increases cost by X, how much additional efficiency do I need to justify the cost?” • Many preset options that are easily tunable / configurable • https://github.com/NREL/PVLCOE • https://www.nrel.gov/pv/lcoe-calculator/ • Poster presentation by Brittany Smith in CO1-3: “Presentation of Fiscal Year 2020 Results from Technoeconomic Analysis for DuraMAT” NREL’s online LCOE calculator allows users to quickly compare the LCOE of proposed systems against a baseline Contact: Brittany Smith
  13. 13. • Software and algorithms for data cleaning – https://github.com/pvlib/pvanalytics [[POSTER: CO1-1, Hansen]] • PV-Terms - a project to unify terminology in software – https://github.com/DuraMAT/pv-terms • Integrating PVDAQ data sets into DataHub – https://pvdata.duramat.org • Tools for string length calculations – https://pvtools.lbl.gov/string-length-calculator • Climate descriptors potentially relevant to solar degradation – https://pvtools.lbl.gov/pv-climate-stressors • Specific techno-economic studies conducted with industry partners – [[POSTER CO1-3, Woodhouse & Smith]] Other Central Data Resource Projects (historical and current)
  14. 14. • The Central Data Resource aims to maximize the potential of applying data as a resource to help solve problems related to solar degradation • We would be happy to hear any ideas you might have for how to extend this initiative! Conclusion DuraMat Data Hub Data Software Analysis Tools
  15. 15. Q&A

Presentation given at the virtual Durable Module Materials (DuraMat) workshop, Sept 2020

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