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Modelling Through High Frequency Data Sampling and Other Advantages

Modelling Through High Frequency Data Sampling and Other Advantages. by Francis Pelletier - PhD Candidate, ETS Christian Masson – PhD Director, ETS Antoine Tahan – PhD Co-Director, ETS Martin Jetté , General Manager, OSIsoft Canada ULC. Presented at :. In collaboration with.

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Modelling Through High Frequency Data Sampling and Other Advantages

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  1. Modelling Through High Frequency Data Sampling and Other Advantages by Francis Pelletier - PhD Candidate, ETS Christian Masson – PhD Director, ETS Antoine Tahan – PhD Co-Director, ETS Martin Jetté, General Manager, OSIsoft Canada ULC Presentedat : In collaboration with École de Technologie Supérieure

  2. PhD Project: Power Performance Evaluation and Improvement of Operational Wind Power Plant Project’s objectives 1. Improve Actual Power Performance Evaluation Techniques (modelling) 2. Improve Power Performance of Operational Wind Power Plant An energy increase of 1% in energy output could represent a financial benefit of 3-4 Millions Euros (100MW Wind Power Plant) /21

  3. Advanced Data acquisitioning system FrequencySampling = 1 Hz Number of tags per second ≈ 100 000 tags PI DiskSpace per year ≈ 0.6 Tbyte / year /21

  4. 1. High Frequency Data Sampling improves Power Curve modelling capabilities /21

  5. Industry-Standard vs High frequency data sampling(10 minute-average VS 1 second) POWER 1 second data output Typical industry-standard data logger output (10 minute-average) TIME Power (10 min) Power (1 sec) /21

  6. Advantages related to high frequency data samplingNew Metrics • ACTUALLY AVAILABLE WITH 10 MINUTE-AVERAGE DATA • AVERAGE10min • STD10min • min10min • max10min • NEW METRICS WITH 1 SECOND DATA SAMPLING • Avg, Std, min, max for period different than 10 minutes • Statistical distribution • Skewness - Kurtosis • Derivatives • Filters / smoothing • Dynamic response • (V10min)2 ≠ (V2)10min • Etc… /21

  7. Advantages related to high frequency data samplingIncreases the Number of Data for Modelling Period: 2009-02-01 00:00:00 to 2010-02-01 00:00:00 2009-02-01 00:00:00 to 2010-02-01 00:00:00 2009-02-01 00:02:00 to 2010-02-01 00:02:00 P10min = 100 kW P10min = 500 kW 00:00:00 00:10:00 00:20:00 ... to 2010-02-01 00:04:00 2009-02-01 00:04:00 2009-02-01 00:06:00 to 2010-02-01 00:06:00 00:05:00 00:15:00 to 2010-02-01 00:08:00 2009-02-01 00:08:00 P10min = 300 kW /21

  8. Power CurveAnalysis – Standard Analysis Tower vibration Anemometer defect Power [kW] Faults and downtime Ice accretion on blades High wind speed cut-out Wind Speed [m/s] /21 Ref.: Jean-Daniel Langlois – GL : GreenPowerConferences – Houston09

  9. Problems faced by O&M personel Power Curve STD>50kW Power [kW] Wind Speed [m/s] /21

  10. Review of literature – Power curve modelling • Discrete models (Bin) • P=f(WS@nacelle) • IEC61400-12-1 • Air density correction • TI correction • Multivariate analysis • IEC61400-12-2 • Parametric models • Polynomial functions • Logistic function with 4 parameters (G.A.) • Power curve partitions (3 regions) • Stochastic models • Markov Chain • Non-parametric models • K-NN • SVM • Boosting Tree • ANN • etc... • Data Reduction Techniques • PCA • Self Organizing Map (SOM) It has been found that the power curve modeling’s precision is difficult to improve because of: - Non-linearities - Interaction between variables /21

  11. Some Results with ANN modeling • P = f (WS & Air Density) Air Density 1.22 kg/m^3 to 1.3 kg/m^3 POWER [kW] Wind Speed [m/s]

  12. Some Results with ANN modeling • P = f (WS & Turbulence Intensity) Turbulence Intensity (TI) 7.5% to 15% POWER [kW] Wind Speed [m/s]

  13. Some Results with ANN modeling • P = f (WS & Wind Shear) Wind Shear (V80/V40) 1.15 to 1.5 POWER [kW] Wind Speed [m/s]

  14. Some Results with ANN modeling • P = f (WS & WSskew) Wind Speed Skewness -0.5 to +0.25 POWER [kW] Wind Speed [m/s]

  15. 2. Other Advantages Related to High Frequency Data Sampling /21

  16. Control validation (Cut-in & Cut-out validation) Wind Speed (1sec) Cut-out ( Lowwind speed) Cut-in Power (1sec) 168.75 137,5 75 43.75 12.5 Cut-in Wind Speed (3.5 m/s) -18.75 Enables: - Yaw validation & Yawerrordetection - Diverter position - Pitch & RPM validation - Faults investigation - Availability validation - etc... /21

  17. Enhanced Trouble Shooting Possibilities INSTANTANEOUS OVER POWER Power (1sec) – Normal Behaviour Power (1sec) – Over Power Maximum Instantaneous Power /21

  18. Advantages related to high frequency data samplingIII- Trouble shooting possibilities POWER T X RPMgenerator ENCODER MALFUNCTION (1 second Data) /21

  19. Other opportunities related to high frequency data sampling • New faults and underperformance investigations techniques • Availability validation • Support for Insurance or legal claims • Ex.: Gear box, blade damage etc… • Advance power curve modelling (Markov chain) • Condition monitoring (ex.: FFT analysis…) • Forecasting Improvement (Ramp) • Improve quality control of data (Outlier’s identification) • Wake’s validation and investigation • Control validation and optimization • Etc… /21

  20. Conclusion • High frequency data sampling improves modelling possibilities • Several other advantages related to high frequency data sampling have been found. • Future investigations will certainly demonstrate new advantages related to high frequency data sampling of wind power plants /21

  21. Merci! Thankyou!

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