Virtual talk given at PVRW, Feb 2021

1. Overview of DuraMat software tool development
Anubhav Jain
Lawrence Berkeley National Laboratory
w/contributions from:
Todd Karin (LBL), Xin Chen (LBL), Thushara Gunda (Sandia), Cliff Hansen (Sandia), Bennet
Meyers (Stanford), Brittany Smith (NREL) and their respective teams

2. Why a talk on software development?
• The “end product” of most conventional
funded research is a paper or report
– This is typically how projects are evaluated
• However, software can also be an
invaluable end product!
– Sometimes, more valuable than text
• Data can also be an end product!
– See poster on DuraMat Data Hub by R. White.
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Given a data set, up to 10 analysts were asked to
calculate the degradation rate of 5 PV systems using
either (i) any approach they wanted, (ii) a documented
standard, or (iii) a documented codebase (RdTools).
Consistent results came only with the codebase,
highlighting the role software can play in
reproducibility and reusability.
Jordan, D. C., Luo, W., Jain, A., Saleh, M. U., von Korff, H., Hu, Y.,
Jaubert, J.-N., Mavromatakis, F., Deline, C., Deceglie, M. G., Nag, A.,
Kimball, G. M., Shinn, A. B., John, J. J., Alnuaimi, A. A. & Elnosh, A. B.
A. Reducing Interanalyst Variability in Photovoltaic Degradation
Rate Assessments. IEEE J. Photovoltaics 10, 206–212 (2020).

3. DuraMat-funded projects are developing software
to solve a spectrum of PV problems
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Core functions common
to many PV analyses
Operation and
degradation in the field
Planning and
reduction of LCOE
pvanalytics
pv-pro data preprocessor
pv-pro
pvOps
pvARC
pv-vision
simple LCOE calculator
vocmax

4. DuraMat software projects share some DNA
• Open-source licenses
– Typically MIT / BSD which are commercial-friendly
• All based on Python / pydata stack for interoperability
– not mixing MATLAB, Python, Java, Excel macros, etc.
• Make use of large data sets and machine learning / statistical learning
– e.g., neural networks for vision, natural language processing for text
• Collaborative and sustainable development via the Github platform
– i.e., NOT a single developer sending around “script_v34” by email
• More integration forthcoming
– e.g., https://duramat.github.io/pv-terms/ to unify common variable names
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5. pvlib/pvanalytics / Cliff Hansen, Will Vining, Matt Muller
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• python library for automating analysis of data from PV systems
• Workflow independent, built up from base functions
• v0.1 released 20 November github.com/pvlib/pvanalytics
• Fully compatible with pvlib/pvlib-python for PV system modeling
Quality control functions
• Plausibility of irradiance and weather measurements
• Identification of missing, interpolated, or stale data
• outlier detection
• Identification of timestamp problems such as daylight
savings shifts
Feature identification functions
• inverter clipping
• clear-sky periods
• day/night detection from power or irradiance
Identification of system properties
• tilt and azimuth from power data
• differentiation between fixed and tracking PV systems
Metrics
• NREL weather corrected performance ratio
v0.1 Content
Core

6. PV-Pro Data Preprocessor /
Bennet Meyers, Todd Karin
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PV-Pro can be found at: https://github.com/DuraMAT/pvpro
• 0: System at maximum power point.
• 1: System at open circuit conditions.
• 2: Clipped or curtailed.
• -1: No power/inverter off
• -2: Other (errors, corrupted data)
def run_preprocess(self,
correct_tz=True,
data_sampling=None,
correct_dst=False,
fix_shifts=True,
max_val=None):
Core

7. PVPRO/ Todd Karin, Anubhav Jain, Bruce King, Michael Deceglie,
Bennet Meyers, Dirk Jordan, Cliff Hanen, Laura Schelhas
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PVPRO be found at: https://github.com/DuraMAT/pvpro
• Use operation data (DC current, DC voltage, module temperature and plane-of-
array irradiance) to determine module single diode model parameters vs. time.
PRODUCTION DATA
Time series
database
PVPRO
• Filter power data
• Meteorological data
• Circuit model
• Parameter estimation
• Uncertainty analysis
I-V Parameters
Rseries, Voc, Isc, Bvoc, …
Degradation Mode Estimates
Soiling, PID, Encpasulant
discoloration, solder bond failure
Output
BIG data analysis
Application
Technology
Degradation
mode
Solder bond damage detected
Methods
Actionable analytics
More accurate
power
predictions
Operation &
Degradation

8. pvOps / Thushara Gunda, Michael Hopwood, Hector Mendoza
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pvOps is currently underdoing review and will be published on GitHub as part of pvlib in April 2021
Module 1: Text
• Date Extraction
• Consistent Labels
• Issue Summary
Module 2: Text2Time
• Date Alignments
• Quantify Impact
• Visualizations
{
“Date_EventStart”: XXX
“Date_EventEnd”:XXX
“Asset”:XXX
“ProductionImpact”:XXX
“Response”:XXX
“IssueDescription”:XXX
}
• Although text-based records can contain valuable contextual information to support data processing and site
assessment activities, significant diversity in these records makes it challenging to ascertain needed insights.
• The pvOps package contains two modules to support: 1) processing of text data and 2) fuse text with timeseries data
• Similar to other pvlib packages, pvOps is written as a series of individual functions that could be integrated using class
wrappers to support individual workflows
Operation &
Degradation

9. PVARC/ Todd Karin, David Miller, Anubhav Jain
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PVARC be found at: https://github.com/DuraMAT/pvarc
• Extract coating parameters from spectral reflectance
data using thin-film interferometry model
“Non-destructive Characterization of Anti-reflection
Coatings on PV Modules” Posted arXiv:2101.05446. (in-
press at JPV).
Spectral Reflectance Measurement of PV
module glass
Quantify anti reflection properties
Operation &
Degradation

10. PV-vision/ Xin Chen, Todd Karin, Anubhav Jain
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PV-vision be found at: https://github.com/hackingmaterials/pv-vision
Solar Cell Crack Detection Algorithm & Data Analytics Automatic Classification of Fire damaged Solar Modules
• Goal: automatically extract features of cracks on solar modules and
quantify the relationship between features and module power loss
• Tool developed for cropping out single cells from solar modules
with accuracy of 90%
• Goal: automatically classify the solar modules based on the
categories of defects which are induced by conflagration
• New tool assisted with CNN developed to do perspective
transform and cell cropping. Accuracy improved and will be
applied to all dataset
crop
• UNet model trained to segment the cracks with overall F1 metric of 0.89
• Initial tool developed to calculate crack features (length, orientation, etc.)
Fig.4 Left: original image. Right: predicted masks.
F1 metric=0.882
vectorized
Fig.5 Left: crack vectors. Middle: poly-fitted
cracks. Right: filter short cracks(length<30)
UNet
Fig.1 Solar module
Fig.2 Cropped solar cells
Fig.3 Masks predicted by UNet model. Left:
original image. Right: masks are highlighted in
different colors. Purple (crack), Green (power
loss area), brown (busbar). F1 metric=0.862
Fig.6 Original, perspective- transformed solar module and cropped cells
transform
(CNN assisted) crop
• Yolo model trained to detect defective cells in the solar
modules
• Solar modules classified based on the number of different
defective cells with accuracy of 98%
Fig.7 Defective cells detected by Yolo model.
Green: crack defect. Blue: Solder defect. Red:
Oxygen induced defect. Purple: Intra-cell defect.
Classification criterion
If no defective cell or ‘crack’ <= 1:
Category 1
If ‘crack’ >= 2 or ‘oxygen’ >= 1:
Category 2
If ‘intra’ >= 1:
Category 3
Operation &
Degradation

11. Simplified PV LCOE Calculator* / Brittany Smith (NREL)
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The simplified PV LCOE calculator can be found at: www.github.com/NREL/PVLCOE
Currently, the calculator offers only two default degradation
rates (based on cell technology selection) and assumes
module degradation approximates system-level degradation.
DuraMAT analysis of PV Fleets data will provide updated
system-level degradation rates and could potentially populate
different default degradation rate values for:
• Cell technology
• Package type
• System type
• Install location
The calculator lets you select
from a set of system options:
Default values
pre-populate
based on selections
Results include LCOE,
module price, system cost
*calculator was originally developed outside of DuraMAT
Planning
& LCOE

12. Vocmax – a string length calculator for reducing LCOE /
Todd Karin, Anubhav Jain
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Conventional string length calculations are unnecessarily
conservative
• Most common practice is to use minimum historical ambient temperature
and 1000 W/m2 for calculating maximum string Voc, but these conditions
do not co-occur in practice.
• Longer strings lower system costs – get more power through the same
wires.
Need a more realistic method for calculating string length
• We developed and validated a method for calculating the string length by
modeling the system VOC over time at the location of interest.
• Method is consistent with NEC 690.7(A)(3) standard.
Impact
• In the US, string lengths increased by 10% on average using site—specific
modeling, leading to a 1.2% reduction in LCOE !
https://github.com/toddkarin/vocmax
https://pvtools.lbl.gov/string-length-calculator
https://ieeexplore.ieee.org/document/9000497
Approach: model the distribution of operating and
open-circuit voltage over time to determine threshold
Planning
& LCOE

13. Conclusions
• In addition to papers and reports, software and data represent important
outputs for research
• DuraMat has a diverse portfolio of software development projects across
the chain of PV operation
• The software is available open-source and is the product of collaborative
development
• We would be very happy to hear from you!
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