The Central Data Resource develops and disseminates solar-related data, tools, and software. It hosts a central data hub that securely stores both private and public data from DuraMat projects. It also develops open-source software libraries that apply data analytics to solve module reliability challenges. The data hub currently has over 60 projects, 128 datasets including 70 public datasets, and over 2000 files and resources accessible to its 137 users.
UCSD NANO 266 Quantum Mechanical Modelling of Materials and Nanostructures is a graduate class that provides students with a highly practical introduction to the application of first principles quantum mechanical simulations to model, understand and predict the properties of materials and nano-structures. The syllabus includes: a brief introduction to quantum mechanics and the Hartree-Fock and density functional theory (DFT) formulations; practical simulation considerations such as convergence, selection of the appropriate functional and parameters; interpretation of the results from simulations, including the limits of accuracy of each method. Several lab sessions provide students with hands-on experience in the conduct of simulations. A key aspect of the course is in the use of programming to facilitate calculations and analysis.
IMPLEMENTATION OF A REAL TIME MONITORING SYSTEM FOR A PHOTOVOLTAIC GENERATION...adeij1
Generally PV generators are considered reliable compared to other systems, but like all processes, a PV system can be exposed to several failures causing the PV system to malfunction. Several studies have found that the reliability of PV systems is highly dependent on the equipment used for the construction of PV panels, temperature, humidity and solar radiation. A PV system can have several defects, be it defects of construction types, or material and electrical defects caused by climatic conditions. As such, we can cite the fault most commonly encountered in a PV generator which is the partial shading defect.
UCSD NANO 266 Quantum Mechanical Modelling of Materials and Nanostructures is a graduate class that provides students with a highly practical introduction to the application of first principles quantum mechanical simulations to model, understand and predict the properties of materials and nano-structures. The syllabus includes: a brief introduction to quantum mechanics and the Hartree-Fock and density functional theory (DFT) formulations; practical simulation considerations such as convergence, selection of the appropriate functional and parameters; interpretation of the results from simulations, including the limits of accuracy of each method. Several lab sessions provide students with hands-on experience in the conduct of simulations. A key aspect of the course is in the use of programming to facilitate calculations and analysis.
IMPLEMENTATION OF A REAL TIME MONITORING SYSTEM FOR A PHOTOVOLTAIC GENERATION...adeij1
Generally PV generators are considered reliable compared to other systems, but like all processes, a PV system can be exposed to several failures causing the PV system to malfunction. Several studies have found that the reliability of PV systems is highly dependent on the equipment used for the construction of PV panels, temperature, humidity and solar radiation. A PV system can have several defects, be it defects of construction types, or material and electrical defects caused by climatic conditions. As such, we can cite the fault most commonly encountered in a PV generator which is the partial shading defect.
A study on modelling and simulation of photovoltaic cellseSAT Journals
Abstract This Paper presents a detailed study on the types of modelling of the PV Panel for simulation studies. The main concern of this study is to analyze the results and compare them under standard test conditions. PV systems are generally integrated with specific control algorithms in order to extract the maximum possible power. Hence it is highly imperative that the Maximum Power Point (MPP) is achieved effectively and thus we need to design a model from which the MPPT algorithm can be realized in an efficient way. Also other parameters should be taken into account for finding the best model for the use in simulation. It is very important to choose the appropriate model based on the application. The models used for study in this paper include the single diode model, two diode model and Simscape modelling. MATLAB/Simulink presents a powerful tool to study such systems. The work tests the accuracy of the models under different temperature and irradiance conditions. The two diode model is known to have better accuracy at low irradiance levels which allows for a more accurate prediction of PV system performance. Simscape, part of Simulink environment, has a solar cell block that makes building a PV model straightforward and much easier programming with full demonstration to all system details. On the basis of the study, the best model that can be used for simulation purposes can be selected. It is envisaged that the work can be very useful for professionals who require simple and accurate PV simulators for their design. All the systems here are modeled and simulated in MATLAB/Simulink environment. Keywords: PV cell, STC, MATLAB Simulink, Ideality Factor
An efficient optical inspection of photovoltaic modules deploying edge detec...IJECEIAES
With the enhanced industrial and domestic energy needs, there is a great urge for renewable energy sources because of their eco-friendly nature. Solar energy is crucial among renewable energy sources and there is a great need to optimize and enhance the performance of solar energy usage that is mainly dependent on the system components. The current work has been aimed to discuss the fault detection of photovoltaic (PV) modules by evaluating an efficient, facile inspection algorithm electrical analysis for real-time applications. The paper presents a real-time experimental model for infrared thermography using a thermal imager mounted on a tripod at a suitable distance from the PV modules to capture the images in the best possible way. A novel hybrid algorithm has been proposed and the fault detection along with the electrical parameter analysis has been accurately performed on the PV modules to analyze and process various externally induced faults in the PV systems.
THERMAL FAULT DETECTION SYSTEM FOR PV SOLAR MODULESelelijjournal
Photovoltaic (PV) modules used to convert sunlight into electricity. PV researches and industries are
rapidly becoming popular in the energy field since PV technologies do not harm to environment and use
sun which is unlimited energy source. Nowadays, many applications are realized with photovoltaic (PV)
modules in different areas such as buildings, aviation, solar power plants, land and sea transportations,
etc. Construction, operation and maintenance of solar PV system are not easy and complex. There are
many methods for PV plants inspection such as visual inspection, using current sensors, comparing the
input and output power units of PV modules, and thermal monitoring with infrared cameras. Monitoring
the differences on the PV module output voltage by means of sensors is the most appropriate methods but it
is very expensive solution since there are thousand PV modules in some plants. Thermal monitoring system
is more suitable method for large PV plants’ inspection. Because, it reduces the fault detection costs and
provide shorten maintenance time. The main aim of this paper is to investigate thermal monitoring of the
PV solar modules and realize image processing by thermal radiation on PV modules. For this purpose, it is
created a wireless directable robotic vehicle which has RF and thermal camera, two brushless hub motor
and X-Bee modules to send direction commands. In this way, the robot moves between the panels and sent
data for user whether there is fault on the panels or not. The test results indicate that PV module faults are
detected effectively by using thermal cameras.
Power Quality Improvement in Grid Connected PV Systemijtsrd
The general trends in the past decade of increasing solar cell efficiency, decreasing PV system costs, increasing government incentive programs, and several other factors have all combined synergistically to reduce the barriers of entry for PV systems to enter the market and expand their contribution to the global energy portfolio. The shortcomings of current inverter functions which link PV systems to the utility network are becoming transparent as PV penetration levels continue to increase. In this paper an analysis of solar system connected with the grid has been done. The system is subjected with two types of perturbations, i.e. variable load and the variable irradiance level which changes the output of the solar system. The design, modeling, and analysis of a grid-tied PV system are performed in the MATLAB software simulation environment. Solar cell works on the principle of photo voltaic effect, which has nonlinear voltage and current characteristics. These characteristics are improved with the help of maximum power point tracking (MPPT) controller. MPPT controller helps to feed the inverter with maximum power from the solar system. Results indicate that in the presence of grid disturbances the inverter can react dynamically to help restore the power system back to its normal state. A harmonic analysis was also performed indicating the inverter under study met the applicable power quality standards for distributed energy resources Anjali | Gourav Sharma"Power Quality Improvement in Grid Connected PV System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-4 , June 2017, URL: http://www.ijtsrd.com/papers/ijtsrd2196.pdf http://www.ijtsrd.com/engineering/electrical-engineering/2196/power-quality-improvement-in-grid-connected-pv-system/anjali
This study investigates experimentally the performance of two-dimensional solar tracking systems with reflector using commercial silicon based photovoltaic module, with open and closed loop control systems. Different reflector materials were also investigated. The experiments were performed at the Hashemite University campus in Zarqa at a latitude of 32⁰, in February and March. Photovoltaic output power and performance were analyzed. It was found that the modified photovoltaic module with mirror reflector generated the highest value of power, while the temperature reached a maximum value of 53 ̊ C. The modified module suggested in this study produced 5% more PV power than the two-dimensional solar tracking systems without reflector and produced 12.5% more PV power than the fixed PV module with 26⁰ tilt angle.
Several algorithms have been offered to track the Maximum Power Point when we have one maximum power point. Moreover, fuzzy control and neural was utilized to track the Maximum Power Point when we have multi-peaks power points. In this paper, we will propose an improved Maximum Power Point tracking method for the photovoltaic system utilizing a modified PSO algorithm. The main advantage of the method is the decreasing of the steady state oscillation (to practically zero) once the Maximum Power Point is located. moreover, the proposed method has the ability to track the Maximum Power Point for the extreme environmental condition that cause the presence of maximum multi-power points, for example, partial shading condition and large fluctuations of insolation. To evaluate the effectiveness of the proposed method, MATLAB simulations are carried out under very challenging circumstance, namely step changes in irradiance, step changes in load, and partial shading of the Photovoltaic array. Finally, its performance is compared with the perturbation and observation” and fuzzy logic results for the single peak, and the neural-fuzzy control results for the multi-peaks.
A Comprehensive Analysis of Partial Shading Effect on Output Parameters of a ...IJECEIAES
One of the issues of grid-connected photovoltaic systems is the effect of the partial shading on the key parameters and performance of the system. In practice, a share of the entire PV panel may shadded because of the various reasons, inevitably. In this case, the key parameters of the system output are affected with respect to the shading extent and paradigm. In this paper, the effects of the various partial shading patterns on the ouput of the system are examined. This is performed by deriving relevant equations and appropriate modeling of the system and defining different scenarios. The analysis on the system performance is carried out on the dominant output parameters including panel voltage, panel power, and total harmonic distortion (THD) of the inverter. Also, the study considers the effect of using bypass diodes in the panels or not. Addintionally, to compare derived conclusions, the study is implementd on a practical system. The set up is made up of a 7-level multilevel inverter, a Z-source converter, and 1 kW lateral circuitry. The real world test results of the study demonstrate a negligible deviation compared to the simulation results.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
Salas, V. (2024) "John of St. Thomas (Poinsot) on the Science of Sacred Theol...Studia Poinsotiana
I Introduction
II Subalternation and Theology
III Theology and Dogmatic Declarations
IV The Mixed Principles of Theology
V Virtual Revelation: The Unity of Theology
VI Theology as a Natural Science
VII Theology’s Certitude
VIII Conclusion
Notes
Bibliography
All the contents are fully attributable to the author, Doctor Victor Salas. Should you wish to get this text republished, get in touch with the author or the editorial committee of the Studia Poinsotiana. Insofar as possible, we will be happy to broker your contact.
Core Objective 1: Highlights from the Central Data Resource
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. 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. 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. “A central data resource that securely
hosts a mix of private and public data of
multiple data types”
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. 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. 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. 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. “Development of open-source software
libraries that apply data analytics to solve
module reliability challenges”
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. 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. 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. • 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. • 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