Post on 07-Aug-2020
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Icing prediction on blade wind turbine using forecast data
From Antoine Amossé, instrumentation and control Engineer
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Summary
1. Context & Challenge
2. Data integration and algorithm
3. Results
4. Conclusion & Further development
5. Nergica
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Cold climate + precipitations
Wind turbine
Icing accretion Context
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Context Icing - Risks for H&S
- Fatigue on structure
- Power loss
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Context
Coating Electrothermal Hot air Solution!!
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Challenge
Forecast Data PI Node/Svr Model/Algorithm Action Notifications
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Forecast data
- Forecast models:
HRDPS High Resolution Deterministic Prediction System
PRIVATE
NAM North American Model
PUBLIC access
+3.5 days +2 days
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Forecast data
FTP site
6h
Station 1
Station 2
Station 3
Updates ~6h
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Integration in PI Server
- Data is imported into PI using Universal File Loader (UFL) interface
Station 1
Station 2
Station 3
PI Server
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Building structure in PI AF
Build an AF Structure to facilitate deployment of analysis and algorithms through multiple sites.
PI Server
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Visualization in PI
Future Now
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Building algorithm
ICING = LOW TEMP. + WATER
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Building algorithm
<TEMP
0degC
>PrecipiationRate
0mm/h
Low Temp.
Water
AND Icing
ICING = LOW TEMP. + WATER
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Building algorithm
Validation in PI AF
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Ice measurement
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Ice measurement
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Data comparison
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Data comparison
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Visualization in PI Vision
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Alerting customer Choose
user/groups Manage format Edit the trigger
12h
1h
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Conclusion • Alerts are sent 12h and 1h before event to users
• Challenges • Data available periodically • Building and optimize algorithm
• PI system allows to • Deployment • Archiving & validation • Review the events
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Future work
• Optimization of algorithms
• Analysis of benefits in $$ or operation time
• Microgrid management based on forecast data
• Renewable production (Wind, solar)
• Storage management (battery, flywheel)
• Load estimation
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Nergica Centre of Applied research that stimulate
innovation in the renewable energy
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Wind Energy Conference
Carleton-sur-Mer, QC, CANADA
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Optional: Click to add a takeaway you wish the audience to leave with.
Thank You
Merci
Grazie
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Conference Theme & Keywords
Icing Forecast
AF structure PI Analytics
Matlab
PI_UFL
Prediction
NAM HRDPS
Cold climate
Québec
Canada
Wind Energy
Solar Energy
Notification
Blade
Wind Turbine
In-site measurement
Icing sensors
Camera system Weather
Nergica
Nergica Nergica
Solar energy
Image Analysis Precipitation
Snow
Freezing rain
Ice pellets
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RESULTS CHALLENGE SOLUTION
COMPANY and GOAL
27 #OSIsoftUC #PIWorld ©2018 OSIsoft, LLC
Icing prediction for blade wind turbine using forecast data
Nergica éolien is a center of expertise that supports the development of the wind industry through research, technology transfer and technical assistance for businesses
Deicing systems installed on wind turbine blades need to be activated prior the meteorological event, in order to prevent icing accretion.
Algorithm based on forecast data are built to detect the beginning of the icing event.
Activating heating system in wind turbine keep the blade hot and avoid ice accretion, up to a given rate
Algorithms are compared with in-site instrumentations
Benefits have not been measure yet
• Integration of forecast data
• Building algorithm to detect meteorological icing
• Send notification prior the event
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#OSIsoftUC #PIWorld ©2018 OSIsoft, LLC
• Antoine Amossé
• aamosse@nergica.com
• Instrumentation and Control Analyst
#OSIsoftUC #PIWorld ©2018 OSIsoft, LLC
#OSIsoftUC #PIWorld ©2018 OSIsoft, LLC
Questions
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