NREL is a national laboratory operated by the Alliance for Sustainable Energy, LLC for the US Department of Energy. The document discusses a new method for monitoring series resistance in photovoltaic systems in real-time without calibration. By observing open-circuit voltage values at different irradiances and temperatures, the method can calculate a time series for series resistance that provides insight into potential problems. This automated monitoring could help issue alerts for failures or conditions that increase fire risks like degraded connections.
You Be the Judge: A Ratings Tool for Selecting the Best Solar Module Rick Borry
What is the best PV module for a particular application? Is it one with the lowest cost per watt? Ultimately, it is the amount of energy produced that is the key factor in the economics of investment recovery and profit.
The Principal Solar Institute (PSI) has developed a tool for analyzing this key element: The PSI PV Module Rating, an energy assessment tool for comparing the Lifetime Energy Production of PV modules over a 25-year period. Using the PSI Rating, solar energy professionals can finally make easy, meaningful energy-economics comparisons of PV modules between manufacturers or within one manufacturer’s product line.
Hear Matt Thompson PhD, Executive Director of the Principal Solar Institute, and Kenneth Allen, COO of Principal Solar, Inc. and Principal Solar Institute Ratings Expert Panelist give an overview of the PSI PV Module Rating and explain how to use the ratings in financial calculations and comparisons of modules and manufacturers. Also, Steven Hegedus, PhD, scientist at the University of Delaware Institute of Energy Conversion, will present an overview of PV module field testing and performance metrics.Then discover specific applications for your business during a LIVE question-and-answer segment following the presentation.
PSI has just published a whitepaper detailing the PSI PV Module Ratings. You should download it free of charge here.
http://www.principalsolarinstitute.org/uploads/custom/3/_documents/PSIRatingsSystem.pdf
Estimating photovoltaic power output under various irradiance levelHaryo Agung Wibowo
This presentation explain how does Solar Cell panel power output are changing as a function of solar illumination. The specific aim of presentation session is to introduce various method in predicting solar cell power output using manufacture data sheet given in STC rating. Six calculation model are chosen and being tested analytically under influence of changing illumination level. The result are then compared with actual PV performance gained from the P-V curve supplied in manufacture data sheet to measure the degree of accuracy from each calculation procedure.
Effect of Temperature on Power Output from Different Commercially available P...IJERA Editor
Photovoltaic (PV) modules are rated at standard test condition (STC) i.e. at irradiance of 1000 W/m2, temperature at 25 0C and solar spectrum of Air Mass 1.5G. The actual output from the PV module in the field varies from its rated output due to change in ambient environmental conditions from the STC. The reduction in output due to temperature is determined by temperature coefficient which varies with the different types of solar module technologies. In this study, temperature coefficient of different types of commercially available solar modules is evaluated. The testing has been carried out at PV test facility of Solar Energy Centre, New Delhi. The modules are selected randomly from various manufactures. It is found that the average temperature coefficient of power for mono-crystalline, multi-crystalline and CdTe based modules are -0.446 %/°C, -0.387 %/°C and -0.172 %/°C respectively. In case of amorphous silicon module, only one sample is measured and the temperature coefficient is -0.234 %/°C. This study shows that the temperature coefficient for mono crystalline silicon module is higher than the other types of solar modules. This study provides an understanding on the variation in energy generation due to temperature correction between different cell technologies.
Development of a Wireless Sensors Network powered by Energy Harvesting techni...Daniele Costarella
Develer Workshop:
A workshop focused on the principles and benefits of applying the Energy Harvesting techniques on Wireless Sensor Networks. The contents come from my Better Embedded 2013 talk.
The PID insulation tester (TOS7210S) is designed based on the insulation resistance tester (TOS7200) to carry out the evaluation of the PID (Potential Induced Degradation) effect of the PV module precisely and efficiently.
Being equipped with the output ability of 2000 V and the ammeter with nA resolution as well as a polarity switching function, the TOS7210S is also applicable not only to the PID evaluation but also the evaluation of the insulators that requires a high sensitivity of measurement. The tester is equipped with the panel memory that is externally accessible and RS232C interface as standard; it can be flexibly compatible with the automated system.
https://www.n-denkei.com/singapore/inquiry/
Similar to 18 deceglie modeling and monitoring rtsr (20)
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
To Graph or Not to Graph Knowledge Graph Architectures and LLMs
18 deceglie modeling and monitoring rtsr
1. NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.
Applying the principles of suns-Voc
to PV system monitoring
Michael G. Deceglie
Timothy J Silverman
Sarah R. Kurtz
2. 2NREL M. Deceglie PV Performance Modeling and Monitoring Workshop 2017
Goal: Rs monitoring
• Beyond performance indices - automated diagnostic alerts
• Provides actionable intelligence about incipient problems such
as failing contacts or connections
• Increases in Rs associated with fire risks
opped by opening the series circuit in which they are
curring.
rallel arcs occur when two different dc polarities
me in close proximity. In a PV module, a parallel arc
n occur due to a ground fault. Parallel arcs are more
fficult to detect and much more difficult to stop, as
e current flow is either directly from plus to minus or
rough a ground loop.
terial selection or module design is going to prevent a
rom catching fire once an arc is sustained because of
mely high temperatures within an arc.
Resistive heating of solder bonds connecting two ribbons
ame cell.
Fig. 6. An arc across the broken interconnect on a cell.
The most likely place for an open circuit to occur
module is at the output leads because:
• These connections are usually not protected by by
diodes;
No material selection or module design is going to prevent a
module from catching fire once an arc is sustained because of
the extremely high temperatures within an arc.
Fig. 4. Resistive heating of solder bonds connecting two ribbons
from the same cell.
Fig. 5. An arc caused by failure of the solder bond that attached an
output lead to the module circuitry.
IV. IMPROVING MODULE DESIGN AND CONSTRUCTION
Wohlgemuth and Kurtz, PVSC 2012
3. 3NREL M. Deceglie PV Performance Modeling and Monitoring Workshop 2017
Automated Rs monitoring
• Avoid hands-on, equipment-intensive
measurements
• Automatically adapt to other changes in the module
o e.g. change in shunt resistance
NREL Image 6326716
4. 4NREL M. Deceglie PV Performance Modeling and Monitoring Workshop 2017
Background: Suns-Voc
• Measure irradiance
dependent Voc values
• Construct an Rs free IV
curve
• Rs from Ohm’s law:
• More accurate than diode
model curve fitting1
• Most often used for
measuring cells
• Recently demonstrated
outdoors on modules and
systems2
1Bowden and Rohatgi, PVSEC 2001 v.2 p.1802
2Forsyth et al., PVSC 2014, p.1928
Rs-free curve
Actual curve
ΔV
5. 5NREL M. Deceglie PV Performance Modeling and Monitoring Workshop 2017
Operating principles
Irradiance E0
Irradiance Eʹ
Isc,0
Translate Voc from low irradiance to:
6. 6NREL M. Deceglie PV Performance Modeling and Monitoring Workshop 2017
Operating principles
Translate Voc from low irradiance to:
7. 7NREL M. Deceglie PV Performance Modeling and Monitoring Workshop 2017
Real-time series resistance (RTSR)
• Observe:
o Operating point current I0
and voltage V0 at
irradiance E0
o Low-irradiance Voc values
• Calculate target irradiance, Eʹ
Irradiance E0
Isc,0
(V0, I0)
8. 8NREL M. Deceglie PV Performance Modeling and Monitoring Workshop 2017
Real-time series resistance (RTSR)
• Observe:
o Operating point current I0
and voltage V0 at
irradiance E0
o Low-irradiance Voc values
• Calculate target irradiance, Eʹ
• Find recent Vʹoc at Eʹ
• Calculate series resistance:
Irradiance E0
Isc,0
(V0, I0)
Irradiance Eʹ
9. 9NREL M. Deceglie PV Performance Modeling and Monitoring Workshop 2017
Real-time series resistance (RTSR)
• Module will generally be cooler
at Eʹ
• Temperature coefficient
depends on irradiance
• Goal: adaptive, calibration-free
Irradiance E0
Isc,0
(V0, I0)
Irradiance Eʹ
10. 10NREL M. Deceglie PV Performance Modeling and Monitoring Workshop 2017
Handling temperature
10 20 30 40
17
18
19
20
21
Module temperature (°C)
V'oc(V)
11. 11NREL M. Deceglie PV Performance Modeling and Monitoring Workshop 2017
Handling temperature
• Look at recent Voc
measurements near
Eʹ
o Here, recent = 1 week
• Regress Voc vs. T
• Extrapolate
12. 12NREL M. Deceglie PV Performance Modeling and Monitoring Workshop 2017
Handling temperature
• Capture Voc vs. T at relevant irradiance
• Automatically adapt to changes in the module
• Look at recent Voc
measurements near
Eʹ
o Here, recent = 1 week
• Regress Voc vs. T
• Extrapolate
Deceglie et al. IEEE J. PV 5:6 p1706 (2015)
13. 13NREL M. Deceglie PV Performance Modeling and Monitoring Workshop 2017
Irradiance dependence of Voc vs T
• Voc temperature coefficient depends on irradiance
• Important to extract the correct value for the target irradiance
• Using the 1-sun value causes ~20% error in series resistance
NREL field data for Si module showing Voc
temperature coefficient for varying irradiance
14. 14NREL M. Deceglie PV Performance Modeling and Monitoring Workshop 2017
Typical target irradiance
• For 1-sun operation, typically comparing Voc points from 50–80 W/m2
• Most of the time, irradiance is all diffuse at these times, limiting
effects of shade
NREL field data for Si module target irradiance
for Voc values for given operating irradiance
15. 15NREL M. Deceglie PV Performance Modeling and Monitoring Workshop 2017
Example application
• Public dataset
• IV curves collected on
modules deployed in Cocoa
FL, Eugene OR, and Golden
CO
• IV curves measured at 5-
minute intervals
• Meteorological monitoring
• RTSR: Use subset of data,
not full IV curve
Marion et al. NREL Report: TP-5200-61610
Contact: bill.marion@nrel.gov
16. 16NREL M. Deceglie PV Performance Modeling and Monitoring Workshop 2017
Generating an Rs time series
For each operating
point:
• Calculate target
irradiance
• Regress Voc vs. T
(at target
irradiance) for prior
week
• Calculate Rs
→ Time series of Rs
17. 17NREL M. Deceglie PV Performance Modeling and Monitoring Workshop 2017
Generating an Rs time series
For each operating
point:
• Calculate target
irradiance
• Regress Voc vs. T
(at target
irradiance) for prior
week
• Calculate Rs
→ Time series of Rs
Rs losses, aggregated weekly
CIGS1
CIGS2
Si
18. 18NREL M. Deceglie PV Performance Modeling and Monitoring Workshop 2017
Rs time series
CIGS1
CIGS2
Si
Ohmic loss
CIGS2
Si
First and final 4 weeks of Rs losses
Beginning End
Can identify increases in Rs
Rs losses, aggregated weekly
CIGS1
CIGS2
Si
Deceglie et al. IEEE J. PV 5:6 p1706 (2015)
19. 19NREL M. Deceglie PV Performance Modeling and Monitoring Workshop 2017
Validation with IV curves
Weekly fractional ohmic loss
IV curves
CIGS1
Can identify increases in Rs
Beginning
End
Si
Deceglie et al. IEEE J. PV 5:6 p1706 (2015)
20. 20NREL M. Deceglie PV Performance Modeling and Monitoring Workshop 2017
Adaptive capability
Weekly fractional ohmic loss
Adapts to other changes in module
IV curves
CIGS1
CIGS1
CIGS2
Si
CIGS2
Beginning
End
Deceglie et al. IEEE J. PV 5:6 p1706 (2015)
21. 21NREL M. Deceglie PV Performance Modeling and Monitoring Workshop 2017
Implementation
• Readily applicable at
module level
o Integrated electronics
o Power optimizers
• Possible at string-
level
o Challenges:
– Variation in
mounting angle
– Partial shading
NREL Image 6326716
22. 22NREL M. Deceglie PV Performance Modeling and Monitoring Workshop 2017
Conclusion – real time series resistance
• Automated,
module/array
integrated
• Adaptive,
calibration-free
• Benefits
o Beyond performance
indices
o Diagnostic
o Early alert for fire
risks / connection
problems
CIGS1
CIGS2
Si
stopped by opening the series circuit in which they are
occurring.
2. Parallel arcs occur when two different dc polarities
come in close proximity. In a PV module, a parallel arc
can occur due to a ground fault. Parallel arcs are more
difficult to detect and much more difficult to stop, as
the current flow is either directly from plus to minus or
through a ground loop.
No material selection or module design is going to prevent a
module from catching fire once an arc is sustained because of
the extremely high temperatures within an arc.
Fig. 4. Resistive heating of solder bonds connecting two ribbons
from the same cell.
23. 23NREL M. Deceglie PV Performance Modeling and Monitoring Workshop 2017
Acknowledgements
• Thank you for insightful discussion:
o Ron Sinton (Sinton Instruments)
o Chris Deline (NREL)
o Bill Marion (NREL)
• Contact: michael.deceglie@nrel.gov
• Further reading:
o Deceglie et al. IEEE J. PV 5:6 p1706 (2015)
• This work was supported by the U.S. Department of Energy under
Contract No. DE-AC36-08GO28308 with the National Renewable
Energy Laboratory. Funding provided U.S. Department of Energy
Office of Energy Efficiency and Renewable Energy Solar Energy
Technologies Office.
Editor's Notes
-utility scale
-residential scale
Transition: The method for achieving this is based on the same principles as suns-Voc method
Graphic, just the Rs-free and Rs 1 sun curves
----- Meeting Notes (6/10/15 17:09) -----
delta V / I = R
View animation in presentation mode
~85 w/m^2
----- Meeting Notes (6/10/15 17:09) -----
clarify the histogram
----- Meeting Notes (6/10/15 17:09) -----
clarify the histogram
----- Meeting Notes (6/10/15 17:09) -----
clarify the histogram
----- Meeting Notes (6/10/15 17:09) -----
string level comments could be in back up
----- Meeting Notes (6/10/15 17:09) -----
Add formula for ohmic loss