DuraMat CO1 Central Data Resource: How it started, how it’s going …
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The document summarizes several projects developed as part of the DuraMat CO1 Central Data Resource initiative to analyze photovoltaic performance and degradation data. A secure data portal was developed that currently hosts data from 239 users and 271 datasets. Software tools were also created, such as pvAnalytics for data cleaning and filtering, pvOps for operational and maintenance data analysis, and pv-vision for electroluminescence image analysis. These open source tools are publicly available and have helped advance the analysis of PV degradation through access to larger datasets. Overall, the projects have established a foundation for ongoing collaborative research on PV performance and lifetime under DuraMat 2.0.
DuraMat CO1 Central Data Resource: How it started, how it’s going …
- 1. DuraMat CO1 Central Data Resource: How it started, how it’s going … Anubhav Jain* LBNL Aug 30, 2022 2016 proposal Now *w/ contributions from: Todd Karin (LBL), Xin Chen (LBL), Robert White (NREL), Thushara Gunda (Sandia), Cliff Hansen (Sandia), Bennet Meyers (Stanford), Brittany Smith (NREL) and their respective teams slides (already) posted to https://hackingmaterials.lbl.gov
- 2. A variety of projects with some common philosophies Core functions and data common to many PV analyses Operation and degradation in the field data hub pvanalytics pv climate zones pv-pro pvOps pvARC pv-vision simple LCOE calculator vocmax Planning and reduction of LCOE Open- source licenses Python / pydata stack Maintain via Github Suitable for large data sets
- 3. DuraMat Data Hub (R. White) Current Consortium Stats 239 86 271 9653 Users Projects Datasets Resources & Files Soiling Data Analysis Optical Mapping EL Crack detection Fleet degradation Summary Impact A secure data portal was developed and deployed at: https://datahub.duramat.org … and populated with DuraMat 1.0 project data! ~10 months usage Citation Indexes and Registries Science Communities and Public Data Hub DOI request applied to archived data file • Increased data visibility via DOIs • Infrastructure synergy with other EMNs • Continues as data platform for DuraMat 2.0 • See also: Energy Material Network Data Hubs Software Platforms for Advancing Collaborative Energy Materials Research https://doi.org/10.14569/ijacsa.2021.0120677 downstream effects of DOI
- 4. pvanalytics (Cliff Hansen, Will Vining, Kirsten Perry, Matt Muller) Summary Impact The pvanalytics project implements a suite of data cleaning and data filtering tools that are available at: https://github.com/pvlib/pvanalytics Automated tests ensure code correctness • Advance state of the art for various data filtering algorithms • Updates functions in pvlib • Provides workflow-independent tools for various downstream analyses Updated clear sky detection submitted for publication (C. Hansen, D. Jordan)
- 5. pvOps (Thushara Gunda, Michael Hopwood, Hector Mendoza) Summary Impact The pvOps projects contains modules for working with O&M text data: • Consistent labels for various types of O&M procedures • Routines for aligning O&M logs to production data https://github.com/sandialabs/pvOps • Allows for large-scale analysis of O&M logs • Better estimates of actual production losses from various mechanisms • Software repository can be expanded as methods improve See also: pvOps: Improving Operational Assessments through Data Fusion. https://doi.org/10.1109/PVSC43889.202 1.9518439 Analysis of PVROM data set (800+ sites) Affected hours (inverter vs tracker vs module) Total production loss (inverter vs tracker vs module) Production loss distribution (inverter issues)
- 6. pv-vision (Xin Chen, Todd Karin, Anubhav Jain) Summary Impact The pv-vision project contains routines for analyzing module EL images and extracting: • Defect classes at the cell level • Pixel-level segmentation of cracks, busbars, and power-loss areas • Feature extraction such as crack length, isolated area as defined by crack/busbar pattern https://github.com/hackingmaterials/pv-vision • Allows for large-scale analysis of EL data, being adoped by industry • Can be used to determine how environment affects crack features which in turn affects power (ongoing PREDICTS2 collaboration) • See also: Automated defect identification in electroluminescence images of solar modules. https://doi.org/10.1016/j.solener.2022.06.031 Effect of fire damage on defect count (>18,000 modules) Correlating power loss w/crack length Detection and classification of defective cells (cell-level) Labeling of crack, dark regions, and busbars (pixel-level) Vectorizing cracks for feature analysis
- 7. vocmax (Todd Karin, Anubhav Jain) Summary Impact The vocmax project allows planners to accurately estimate string lengths to improve overall LCOE • Conventional methods evaluates Voc at minimum historical ambient temperature AND irradiance of 1000 W/m2 (extremely conservative) • Vocmax models the Voc and Voperating at the desired location to produce a distribution of expected voltages – leading to longer strings https://github.com/toddkarin/vocmax Web tool at: https://pvtools.lbl.gov/string-length-calculator • In the US, modeled string lengths increased by 10% using site—specific modeling, leading to potentially 1.2% reduction in LCOE ! • Article in Solar Power World online, continued interest by IEs • See also: Photovoltaic String Sizing Using Site-Specific Modeling. https://doi.org/10.1109/JPHOTOV.2020.2969788 Distribution of increased string sizes for various criteria vs standard Distribution of simulated voltages as compared to various conventional thresholds
- 8. Other DuraMat 1 Central Data Resource Projects PV Climate Zones Comparative LCOE calculator pvarc pvpro https://github.com/DuraMAT/pvarc https://github.com/DuraMAT/pvpro www.github.com/NREL/PVLCOE Perform instantaneous technoeconomic analyses – e.g., break-even cost analysis of new technologies based on performance and system specs. Given DC voltage/current & environmental data as input, PVPro determines time-dependent values of single diode parameters to understand root causes of power degradation. Method for field estimation of ARC thickness using an integrating sphere, dSLR, or even phone camera Spectrum / color of reflected light indicates ARC condition Defines climate zones more relevant to PV degradation than Koppen- Geiger Used by others to analyze PVFleets & PVROM data Photovoltaic degradation climate zones. https://doi.org/10.1109/PVSC40753.2019.8980831 https://github.com/toddkarin/pvcz Photovoltaic module antireflection coating degradation survey using color microscopy and spectral reflectance https://doi.org/10.1002/pip.3575 Nondestructive Characterization of Antireflective Coatings on PV Modules. https://doi.org/10.1109/JPHOTOV.2021.3053482
- 9. • DuraMat 1.0 funded many projects that advance how PV degradation is analyzed with data and software • These projects were not one-off studies, rather they all have associated public code • Many of these new software implementations will be leveraged going into DuraMat 2.0 Conclusions
- 10. Q&A slides (already) posted to https://hackingmaterials.lbl.gov