Icing prediction on blade wind turbine using forecast data

Transcription

1 Icing prediction on blade wind turbine using forecast data From Antoine Amossé, instrumentation and control Engineer

2 Summary 1. Context & Challenge 2. Data integration and algorithm 3. Results 4. Conclusion & Further development 5. Nergica

3 Context Icing accretion Cold climate + precipitations Wind turbine

4 Context Icing - Risks for H&S - Fatigue on structure - Power loss

5 Context Coating Electrothermal Hot air Solution!!

6 Challenge Forecast Data PI Node/Svr Model/Algorithm Notifications Action

7 Forecast data - Forecast models: NAM HRDPS North American Model High Resolution Deterministic Prediction System +3.5 days +2 days PUBLIC access PRIVATE

8 Forecast data Updates ~6h FTP site 6h Station 1 Station 2 Station 3

9 Integration in PI Server - Data is imported into PI using Universal File Loader (UFL) interface Station 1 Station 2 Station 3 PI Server

10 Building structure in PI AF Build an AF Structure to facilitate deployment of analysis and algorithms through multiple sites. PI Server

11 Visualization in PI Now Future

12 Building algorithm ICING = LOW TEMP. + WATER

13 Building algorithm ICING = LOW TEMP. + WATER TEMP 0degC PrecipiationRate 0mm/h < > Low Temp. Water AND Icing

14 Building algorithm Validation in PI AF

15 Ice measurement

16 Ice measurement

17 Data comparison

18 Data comparison

19 Visualization in PI Vision

20 Alerting customer Edit the trigger Choose user/groups Manage format 1h 12h

21 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

22 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

23 Nergica Centre of Applied research that stimulate innovation in the renewable energy

24 Wind Energy Conference Carleton-sur-Mer, QC, CANADA

25 Merci Thank You Grazie Optional: Click to add a takeaway you wish the audience to leave with.

26 Conference Theme & Keywords Icing Forecast In-site Nergica measurement Matlab Freezing rain Wind Turbine Ice pellets NAM HRDPS Solar Energy Nergica AF structure PI Analytics Nergica Snow PI_UFL Blade Solar energy Canada Icing sensors Precipitation Prediction Cold climate Weather Image Analysis Camera system Notification Québec Wind Energy

27 Icing prediction for blade wind turbine using forecast data COMPANY and GOAL Nergica éolien is a center of expertise that supports the development of the wind industry through research, technology transfer and technical assistance for businesses CHALLENGE Deicing systems installed on wind turbine blades need to be activated prior the meteorological event, in order to prevent icing accretion. SOLUTION Algorithm based on forecast data are built to detect the beginning of the icing event. Integration of forecast data Building algorithm to detect meteorological icing Send notification prior the event RESULTS 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 27

28 Antoine Amossé Instrumentation and Control Analyst

29 Questions Please wait for the microphone before asking your questions Please remember to Complete the Online Survey for this session State your name & company