Systematic Approaches to Ensure Correct Representation of Measured Multi-Irra...Kenneth J. Sauer
Presented at the Sandia Photovoltaic PV Performance Modeling Collaborative (PVPMC) Workshop in May 2013. The concepts of self-reference irradiance, Isc linearity checks, and deviations in efficiency relative to reference conditions (and associated adjustments involving all three) are introduced in addition to the primary topic at hand (PVsyst one-diode model parameter optimization based on measured data). A common vernacular for the deviation in efficiency Eta relative (Rel.) to reference conditions is "delta Eta Rel." (if reference conditions are STC, then it's "delta Eta Rel. STC"). However, in reality, it's "dev Eta Rel." & "dev Eta Rel. STC", respectively.
Revisiting the Model Parameters of an Existing System Using the Photovoltaic ...Kenneth J. Sauer
This is the Amplify Energy version of the presentation (made available after the public announcement of the spin-off of Amplify Energy from Yingli Americas).
Systematic Approaches to Ensure Correct Representation of Measured Multi-Irra...Kenneth J. Sauer
Presented at the Sandia Photovoltaic PV Performance Modeling Collaborative (PVPMC) Workshop in May 2013. The concepts of self-reference irradiance, Isc linearity checks, and deviations in efficiency relative to reference conditions (and associated adjustments involving all three) are introduced in addition to the primary topic at hand (PVsyst one-diode model parameter optimization based on measured data). A common vernacular for the deviation in efficiency Eta relative (Rel.) to reference conditions is "delta Eta Rel." (if reference conditions are STC, then it's "delta Eta Rel. STC"). However, in reality, it's "dev Eta Rel." & "dev Eta Rel. STC", respectively.
Revisiting the Model Parameters of an Existing System Using the Photovoltaic ...Kenneth J. Sauer
This is the Amplify Energy version of the presentation (made available after the public announcement of the spin-off of Amplify Energy from Yingli Americas).
Recent advances in semiconductor technology show the improvement of fabrication on
electronics appliances in terms of performance, power density and even the size. This great achievement
however led to some major problems on thermal and heat distribution of the electronic devices. This
thermal problem could reduce the efficiency and reliability of the electronic devices. In order to minimize
this thermal problem, an optimal cooling techniques need to be applied during the operation. There are
various cooling techniques have been used and one of them is passive pin fin heat sink approach. This
paper focuses on inline pin fin heat sink, which use copper material with different shapes of pin fin and a
constant 5.5W heat sources. The simulation model has been formulated using COMSOL Multiphysics
software to stimulate the pin fin design, study the thermal distribution and the maximum heat profile.
Theoretical heat conduction model development of a Cold storage using Taguch...IJMER
In this project work a mathematical heat conduction model of a cold storage (with the help of
computer program; and multiple regression analysis) has been proposed which can be used for further
development of cold storages in the upcoming future. Taguchi L27 orthogonal array (OA) has been used as
a design of experiments (D.O.E). Heat gain (Q) in the cold room taken as the output variable of the study.
With the help of a computer program several data sets have been generated on the basis of the proposed
model. From the graphical interpretation, the critical values of the predictor variables also proposed so
as the heat flow from the outside ambience to the inside of the cold room will be minimum. Insulation
thickness of the side walls (TW), area of the wall (AW), and insulation thickness of the roof(TR) have been
chosen as predictor variables of the study.
Eternal Sun Group - Bifacial measurements, towards a new norm!Marcello Passaro
ITRPV and customers feedback show that there is a shift towards bifacial modules, however a concern on how to correctly test bifacial modules. Eternal Sun Group has together with research and development institutes like ECN looked at the complications and implications hereof.
Simulation of Laser Thermal Interaction with Titanium Dioxide /Polyvinyl Alco...Editor IJCATR
The aim of this work is to use the computational simulation to define the operational conditions to achieve the desired process. The diagnostic tests were used to guide the experiment where PVA composites doped with Titanium dioxide nanoparticles were irradiated with nitrogen laser in order to modify its properties. The temperature of the samples with different laser fluencies were simulated using finite element method, in COMSOL program, to predict the fluencies that is suitable to use for modification before reaching the decomposition temperature of the nanocomposite sample to make sure not to cause any damage. The optical and thermal properties were experimentally studied, and the results were used to define the absorption coefficient and the thermal conductivity of the studied nanocomposites
Simulation of Laser Thermal Interaction with Titanium Dioxide /Polyvinyl Alco...Editor IJCATR
The aim of this work is to use the computational simulation to define the operational conditions to achieve the desired process. The diagnostic tests were used to guide the experiment where PVA composites doped with Titanium dioxide nanoparticles were irradiated with nitrogen laser in order to modify its properties. The temperature of the samples with different laser fluencies were simulated using finite element method, in COMSOL program, to predict the fluencies that is suitable to use for modification before reaching the decomposition temperature of the nanocomposite sample to make sure not to cause any damage. The optical and thermal properties were experimentally studied, and the results were used to define the absorption coefficient and the thermal conductivity of the studied nanocomposites
Analysis of industrial flame characteristics and constancy study using image ...BIBHUTI BHUSAN SAMANTARAY
The study of characterizing and featuring different kinds of flames has become more important than ever in order to increase combustion efficiency and decrease particulate emissions, especially since the study of industrial flames requires more attention. In the present work, different kinds of combustion flames have been characterized by means of digital image processing (DIP) in a 500 kW PF pilot swirl burner. A natural gas flame and a set of pulverized fuel flames of coal and biomass have been comparatively analyzed under co-firing conditions. Through DIP, statistical and spectral features of the flame have been extracted and graphically represented as two-dimensional distributions covering the root flame area. Their study and comparison leads to different conclusions about the flame behavior and the effect of co-firing coal and biomass in pulverized fuel flames. Higher oscillation levels in co-firing flames versus coal flames and variations in radiation regimen were noticed when different biomasses are blended with coal and brought under attention.
46 optimization paper id 0017 edit septianIAESIJEECS
This paper is a comparisation study between an experimental data and Matlab simulation of output PV characteristic affected by the orientation and the tilt angle of a photovoltaic solar module with inclined plane and by the dimension of the panel. The PV panel was rotated towards the east, south and west and positioned for the angles 0°, 30°, 45°, 60° and 90°. In this position, the values of current, voltage and power are measured. In the other side, using the mathematical model to calculate the solar radiation incident on an inclined surface as a function of the tilt angle was developed in MATLAB/SIMULINK model. The optimum angles were determined as positions in which maximum values of solar irradiation and maximum power were registered to characterize the P-V and V-I photovoltaic panel.
Fire Resistance of Materials & Structures - Modelling of Fire ScenarioArshia Mousavi
A library room, whose structural elements are to be checked (in terms of bearing capacity, R criterion) in fire conditions.
The active protection measures of the room are as follows:
· NO automatic fire suppression;
· NO independent water supplies;
· Automatic detection and alarm systems, by smoke;
· NO automatic transmission to Fire Brigade;
· NO on site Fire Brigade.
· The library is provided with safe access routes and fire-fighting devices.
The thermal characteristics of the walls, floor and ceiling (thick layers) are as follows:
· Mass per unit volume: ρ = 1100 · (1 + F/50) [kg/m3]
· Specific heat: c = 950 [J/ (kg K)]
· Thermal conductivity: λ = 0.5 · (1 - L/50) [W/ (m K)]
Evaluate the possible fire scenario, in terms of temperature-time curve, following:
a) The parametric approach is given in the standard EN 1991-1-2 (with two alternative cooling stages);
b) The two/one-zone numerical model implemented in the Ozone 2.2.5 software according to the two following hypotheses for the vents opening (according to the Luxembourg Authorities):
- Scenario 1: windows are constantly 90% open from the beginning of the fire
- Scenario 2: double glazing failure: 50% opening beyond 200°C and 90% opening beyond 400°C
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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.
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
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
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.
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.
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.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
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The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
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
AI for Every Business: Unlocking Your Product's Universal Potential by VP of ...
14 presentation barykina
1. 30.03.2017 7th
PVPMC Workshop, Lugano, Switzerland
Modeling of PV module temperature using
steady-state models: analysis for different
climates
Elena Barykina, CvO University Oldenburg, Energy Meteorology Group
2. Outline
1. Motivation
2. Steady-state models
3. IEC 61853 part 2: Faiman model
4. Faiman parameters: sensitivity analysis for sites
with different climates
5. Model comparison
6. Conclusions
30.03.2017 7th
PVPMC Workshop, Lugano, Switzerland
3. 30.03.2017 7th
PVPMC Workshop, Lugano, Switzerland
Motivation
PV performance modeling step
Solar Irradiance
Ambient Temperature Wind Speed
Module Temperature
Mounting, module physical properties
4. 30.03.2017 7th
PVPMC Workshop, Lugano, Switzerland
Motivation
PV module temperature models
Steady-state Dynamic
Thermal
response
Neglect t ~ 7-10 min
Math Simplified heat transfer equations Heat transfer equations
Easy to use Numerical solution is needed
Model parameters can be site
specific: measurements are
required
Module layers physical properties
are required
Input data Low resolution ( weather
prediction data, satellite images)
High resolution data
5. Motivation
30.03.2017 7th
PVPMC Workshop, Lugano, Switzerland
Steady-state model: Faiman (IEC 61853-2)
Parameters of the model can be found in literature
or fitted from data
How suitable are the parameters for a particular
location and module technology?
PV performance modeling with input data from weather
prediction models and satellite retrieved irradiance
6. Data
30.03.2017 7th
PVPMC Workshop, Lugano, Switzerland
5 sites with different climate (PVKLIMA project)
Site
Tilt <Tmod>, °C <Gpoa>,
W/m²
Cologne, DE 35° 21 297
Ancona, IT 35° 25 382
Tempe, US 33.5° 39 565
Thuwal, SA 25° 33 522
Chennai, IN 15° 33 440
Estimation of model parameters: 30-sec data (2 weeks, 6 months)
Model validation: 15-min averages (1 year)
Parameters are defined for 4 technologies: polySi, CdTe, CIGS, aSi
10. 30.03.2017 7th
PVPMC Workshop, Lugano, Switzerland
Steady-state models
Mattei model
Sandia model
Tmod=
U PV Tamb +GPOA (ta−nSTC TSTC )
U PV + ßSTC nSTC GPOA
,
U PV=26.6+2.3V wind ,
U PV=24.1+2.9Vwind .
Tmod=Tamb +GPOA exp(a+bV wind).
(3)
(3a)
(3b)
(4)
11. 30.03.2017 7th
PVPMC Workshop, Lugano, Switzerland
IEC 61853-2: Faiman model
Faiman model Tmod=Tamb +
GPOA
u0+u1V wind
.
Fig.3. Gpoa/(Tmod-Tamb) plotted against the wind speed for two weeks of
measurements and corresponding linear fit to the data for polySi module
12. 30.03.2017 7th
PVPMC Workshop, Lugano, Switzerland
IEC 61853-2: Faiman model
Data filters
It is recommended to use 5-sec temporal resolution
measurements and at least 10 days of data
- Low irradiance filter
Irradiance values below 400 W/m²
- Irradiance fluctuations filter
Irradiance values in 10 min after the irradiance varies by more than
±10% from the maximum to minimum value during the preceding 10 min
- Wind fluctuations and gusts filter
Wind speed values in a 10-min interval after and including deviations
below 0.25 m/s and gusts larger than +200% from a 5-min running
average
- Low and high wind speed filter
Wind speed data when the 5-min running average is less than 1 m/s and
greater than 8 m/s
13. 30.03.2017 7th
PVPMC Workshop, Lugano, Switzerland
Note: we used 30-sec measurements
Irradiance fluctuations filter was modified:
reject G(i) and next 10 minutes if
IEC 61853-2: Faiman model
where ∆G(i) = G (i) − G (i − 1) – irradiance increments,
∆G = Gmax − Gmin – is the difference between maximum and minimum values
within the preceding 10 minutes
|∆G(i) | > max(5 W/m² , 0.1 * ∆G ),
14. 30.03.2017 7th
PVPMC Workshop, Lugano, Switzerland
Faiman parameters
After filtering we can define the Faiman
parameters using linear fits
The Faiman parameters defined from two weeks (April) and six months of
measurements for polySi module.
Site u0, W/°C m²
two weeks
u0, W/°C m²
six months
u1, Ws/°C m³
two weeks
u1, Ws/°C m³
six months
Colgone, DE 34.7 35.7 7.78 8.22
Ancona, IT 41.2 41.9 3.20 3.95
Tempe, US 36.4 32.1 4.51 6.08
Thuwal, SA 31.8 39.7 5.61 3.06
Chennai, IN 28.6 30.1 4.45 4.75
mean 34.5 35.9 4.44 4.46
std 4.8 5.0 1.0 1.28
std, % 14 14 22.5 29
15. 30.03.2017 7th
PVPMC Workshop, Lugano, Switzerland
Faiman parameters
Fig.4. Contour plots for rmse and mbe of the modeled module temperature for polySi module at site
Cologne and the Faiman parameters.
- two weeks
- six months
- average over all sites
- literature values
16. 30.03.2017 7th
PVPMC Workshop, Lugano, Switzerland
Faiman parameters
Fig.5. Contour plots for rmse and mbe of the modeled module temperature for CIGS module at site
Tempe and the Faiman parameters.
- two weeks
- six months
- average over all sites
- literature values
17. 30.03.2017 7th
PVPMC Workshop, Lugano, Switzerland
Faiman parameters
1. The similar behaviour is observed for all sites and
modules: 'best performance ellipse' 2-2.5 °C rmse
2. Exact values of the Faiman parameters are not
crucial for the accuracy of the modeled module
temperature
3. Although the values derived from 2 weeks and 6
months differ from each other they give the same
accuracy of the modeled module temperature for the
whole year
18. 30.03.2017 7th
PVPMC Workshop, Lugano, Switzerland
Model comparison
Fig.6. Rmse (left) and mbe (right) of the modeled module temperature using different
steady state models: Standard (NOCT), Skoplaki (SK), Mattei (M1, M2), Sandia (SA),
Faiman (FA)
21. 30.03.2017 7th
PVPMC Workshop, Lugano, Switzerland
Conclusions
1. Faiman parameters are site specific
2. Representative dataset for a given site
results in reliable values of parameters
3. The steady-state models have similar
performance
22. 30.03.2017 7th
PVPMC Workshop, Lugano, Switzerland
Acknowledgements
The work is funded by the German Federal Ministry for Economic
Affairs and Energy (BMWi FKZ 0325517A).
TÜV Rheinland, Markus Schweiger and Werner Herrmann
Literature
The results are published: Barykina, E., Hammer, A., 2017. Modeling of
photovoltaic module temperature using Faiman model: Sensitivity analysis
for different climates. Sol. Energy 146, 401-416.
Literature values of Faiman parameters: Koehl, M., Heck, M., Wiesmeier,
S., Wirth, J., 2011. Modeling of the nominal operating cell temperature
based on outdoor weathering. Sol. Energy Mater. Sol. Cells 95, 1638-
1646.