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Wind Loads Analysis for Drivetrain Modeling

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Today, Wind Turbine OEMS utilize design load cases for the design and certification of wind turbines. These load cases are applied to ensure that turbines operate in a stochastic environment for at least 20 years.

However, reliability analysis should not stop at the design stage. Once a turbine is commissioned, consequent reliability analysis should also be conducted in order to understand how long a turbine will truly last at an installed location with the exposed environmental conditions.

View the Webinar Recording at:
http://sentientscience.com/resource-library/videos/webinar-recordings/wind-load-analysis-drivetrain-reliability/

Published in: Engineering
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Wind Loads Analysis for Drivetrain Modeling

  1. 1. © 2016 Sentient Science Corporation – Confidential & Proprietary Wind Load Analysis for Drivetrain Reliability
  2. 2. © 2016 Sentient Science Corporation – Confidential & Proprietary Host Natalie Hils Director, of Revenue Marketing nhils@sentientscience.com +1 716.807.8655 Wind Load Analysis for Drivetrain Reliability
  3. 3. © 2016 Sentient Science Corporation – Confidential & Proprietary Webinar Instructions Wind Load Analysis for Drivetrain Reliability
  4. 4. © 2016 Sentient Science Corporation – Confidential & Proprietary DigitalClone® Material Science Differentiation ☑ Extend Life☑ Root Causes☑ Long-Term Forecast Wind Load Analysis for Drivetrain Reliability
  5. 5. © 2016 Sentient Science Corporation – Confidential & Proprietary Sentient Science Customers Wind Load Analysis for Drivetrain Reliability
  6. 6. © 2016 Sentient Science Corporation – Confidential & Proprietary Outline • Background and motivation for wind turbine system modeling • Details on Sentient’s wind turbine system modeling approach • Blade and tower reverse engineering • Controller configuration and tuning • Turbine-level modeling • Translation of turbine model to prediction of turbine component loads and life • Validation of turbine modeling • Summary Wind Load Analysis for Drivetrain Reliability
  7. 7. © 2016 Sentient Science Corporation – Confidential & Proprietary Wind Loads Challenge Need to understand the impact loading events have on each unique asset to predict when subcomponents are going to fail Currently in Industry: 1. Wind Loads are captured on a wind farm level basis, not turbine by turbine basis 2. Used in design stage, not during operation 3. Majority of SCADA is under-utilized due to large amount of data produced Wind Load Analysis for Drivetrain Reliability
  8. 8. © 2016 Sentient Science Corporation – Confidential & Proprietary Models Applications Wind Load Analysis for Drivetrain Reliability Component Type Uprate/ Derate Supply Chain Forecasting Operation & Maintenance Planning Turbine Analysis for Repowering Asset Life Prediction
  9. 9. © 2016 Sentient Science Corporation – Confidential & Proprietary Presenter Dr. Adrijan Ribaric Vice President, Systems Technology aribaric@sentientscience.com Wind Load Analysis for Drivetrain Reliability
  10. 10. © 2016 Sentient Science Corporation – Confidential & Proprietary Wind Turbine System Dynamics • Usually used at Design Stage • Important to understand loads and reliability of all components • Important to material-based as well Wind Load Analysis for Drivetrain Reliability
  11. 11. © 2016 Sentient Science Corporation – Confidential & Proprietary Poll Question Wind Load Analysis for Drivetrain Reliability
  12. 12. © 2016 Sentient Science Corporation – Confidential & Proprietary Overview of Sentient’s Turbine Modeling Approach Wind Load Analysis for Drivetrain Reliability 1.Wind Speed 2.Wind Shear 3.Turb. Intensity 4.Air Density 5.Wind Direct. HT 6.Wind Direct. VT 1.Rotor Speed 2.Gen. Power 3.Blade Pitch Wind Data 1.Fx (Thrust) 2.Fy 3.Fz (Weight) 4.Mx (Torque) 5.My (Yaw Moment) 6.Mz (Pitch Moment) 1.Rotor Speed 2.Gen. Power 3.Blade Pitch Turbine Loads input output 1.Blade Moments 2.Blade Deflections Compare, measured vs simulated • Turbine model simulates the interaction between inertial, elastic, and aerodynamic forces • Turbine model is based on the aeroelastic software package FAST (NREL)
  13. 13. © 2016 Sentient Science Corporation – Confidential & Proprietary Inputs to Turbine System Model: Blades Wind Load Analysis for Drivetrain Reliability • Information on blade weight, length, and twist is determined from turbine specifications • The blade is then re-engineered to match the efficiency and power curves as calculated from the turbine SCADA history • After achieving a close blade match, the parameters of the re-engineered blade are implemented in the turbine model
  14. 14. © 2016 Sentient Science Corporation – Confidential & Proprietary Inputs to Turbine System Model: Rotor Blades Wind Load Analysis for Drivetrain Reliability • Blade polars – lift and drag coefficients as a function of angle of attack and Reynolds number • Mass and stiffness distribution along the length of the blade
  15. 15. © 2016 Sentient Science Corporation – Confidential & Proprietary Inputs to Turbine System Model: Rotor Blades Wind Load Analysis for Drivetrain Reliability • Blade polars – lift and drag coefficients as a function of angle of attack and Reynolds number • Mass and stiffness distribution along the length of the blade
  16. 16. © 2016 Sentient Science Corporation – Confidential & Proprietary Inputs to Turbine System Model: Turbine Controller Settings Wind Load Analysis for Drivetrain Reliability Control Law Turbine β* τg ωdesired ωmeasured Control Law Turbine β τg,rated ωdesired ωmeasured Generator Torque Controller Blade Pitch Controller Annoni, 2016
  17. 17. © 2016 Sentient Science Corporation – Confidential & Proprietary Inputs to Turbine System Model: Turbine Controller Settings Wind Load Analysis for Drivetrain Reliability 0 200 400 600 800 1000 1200 1400 1600 0 5 10 15 20 25 MeanPower(kW) Wind Speed (m/s) Published Power Curve Power Curve from SCADA Power Curve from Turbine Modeling
  18. 18. © 2016 Sentient Science Corporation – Confidential & Proprietary Inputs to Turbine System Model: Tower Bending Properties Wind Load Analysis for Drivetrain Reliability 0 10 20 30 40 50 60 70 80 0 100 200 300 TowerHeight[m] Stiffness [GPa] Bending Torsion
  19. 19. © 2016 Sentient Science Corporation – Confidential & Proprietary Poll Question Wind Load Analysis for Drivetrain Reliability
  20. 20. © 2016 Sentient Science Corporation – Confidential & Proprietary Turbine System Model - Workflow Time series of hub loads: 1. Fx (Thrust) 2. Fy (lateral) 3. Fz (weight) 4. Mx (Torque) 5. My (Pitch) 6. Mz (Yaw) Turbine Specific (dynamic, 10 min stat.) 1. Wind Speed 2. Wind Direction 3. Yaw position 4. Operation State AEROELASTIC MODEL Wind turbine (10 min simulation) Controller Site Specific (static) 1. Roughness Length 2. Upflow angle 3. Wind Shear Exponent (annual) 4. Airdensity (annual) Wind Pre-Processing (dynamic, 10 min stat.) 1. Wind Speed 2. Turbulence Intensity 3. Wind shear 4. Air density 5. Inflow horizontal 6. Inflow vertical Wind Load Analysis for Drivetrain Reliability Met-Tower (dynamic, 10 min stat.) 1. Wind Shear Exponent 2. Airdensity 3. Wind Direction 4. Turbulence Intensity Wind MODEL Full-field flow (10 min simulation) (source: Erich Hau, “Windkraftanlagen”, Springer Verlag, 1988)
  21. 21. © 2016 Sentient Science Corporation – Confidential & Proprietary Translation of Turbine Model Outputs into Applied Loads on Drivetrain • Fx = thrust load • Fy = horizontal load • Fz = vertical load (weight) • Mx = Torque • My = overturning moment (induced by weight and wind shear) • Mz = yaw moment (e.g., induced by turbulence) Loads (Fx, Fy, Fz, Mx, My, Mz) from rotor blades Main bearing Main shaft Gearbox Wind Load Analysis for Drivetrain Reliability
  22. 22. © 2016 Sentient Science Corporation – Confidential & Proprietary Turbine Component Analysis - Bearing Analysis Tool (BAT) Wind Load Analysis for Drivetrain Reliability sliding velocity distribution load distribution dynamic motion data history of all internal components orientation, angular velocity and acceleration, torque position, velocity and acceleration, force component interaction data steady or transient operation
  23. 23. © 2016 Sentient Science Corporation – Confidential & Proprietary Turbine Component Analysis – Gear Application Program (GAP) Variable Pinion Wheel Number of Teeth 16 70 Normal module (mm) 14 Normal Pressure Angle (deg.) 20 Normal Helix Angle (deg.) 11 Outside Diameter (mm) 278.75 1067.05 Root Diameter (mm) 201.65 996.93 Profile shift 0.7815 1.394 Fillet Radius (mm) 3.5 5.6 Face Width (mm) 275 265 Tip Relief Length (mm) 22.44 22.44 Tip Relief Magnitude (mm) 0.076 0.076 Root Relief Length (mm) 0 0 Root Relief Magnitude (mm) 0 0 Flank End Relief Length (mm) 27.5 13.25 Flank End Relief Magnitude (mm) 0.009 0.015 Crowning Magnitude (mm) 0.034 0.014 Young’s Modulus (Mpa) 206000 206000 Poisson Ratio 0.3 0.3 Center Distance (mm) 640 Gears Axial Offset (mm) 0 Misalignment (mrad) 0.053 Speed (rpm) 380.00 - Torque (N.m) 55249 - Wind Load Analysis for Drivetrain Reliability
  24. 24. © 2016 Sentient Science Corporation – Confidential & Proprietary Validation of Turbine Load Model For a select set of turbines, loads on the main shaft were physically measured and compared to the simulation The captured loads are: o 𝜔 = Speed o Mx = torque o My = overturning moment (e.g., induced by weight and wind shear) o Mz = vertical moment (e.g., induced by turbulence) Wind Load Analysis for Drivetrain Reliability 1.5MW turbine
  25. 25. © 2016 Sentient Science Corporation – Confidential & Proprietary Validation of Turbine Load Model • Two strain gauges to measure bending moment (separated by 90o) • One strain gauge to measure torque moment • One accelerometer to capture hub speed Wind Load Analysis for Drivetrain Reliability Sensor RS GS
  26. 26. © 2016 Sentient Science Corporation – Confidential & Proprietary Validation of Turbine Load Model Wind Load Analysis for Drivetrain Reliability 0 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 500 600 RotorTorque[kNm] Time [s] SCADA Sensor Turbine Model
  27. 27. © 2016 Sentient Science Corporation – Confidential & Proprietary Validation of Turbine Load Model Wind Load Analysis for Drivetrain Reliability 0 5 10 15 20 25 0 20 40 60 80 100 120 HubSpeed[RPM] Time [s] SCADA Sensor Turbine Model
  28. 28. © 2016 Sentient Science Corporation – Confidential & Proprietary Validation of Nacelle Wind Measurement as Modeling Input Wind Load Analysis for Drivetrain Reliability TurbineMeteorological Tower 0 2 4 6 8 10 12 0 5 10 15 20 25 30 Frequency[%] Wind Speed [m/s] Met-Tower (45m) Nacelle (80m)
  29. 29. © 2016 Sentient Science Corporation – Confidential & Proprietary Validation of Nacelle Wind Measurement as Modeling Input Wind Load Analysis for Drivetrain Reliability TurbineMeteorological Tower 0 10 20 30 40 50 60 70 0 5 10 15 20 25 30 TurbulenceIntensity[%] Wind Speed [%] Met-Tower (45m) Nacelle (80m)
  30. 30. © 2016 Sentient Science Corporation – Confidential & Proprietary Validation of Nacelle Wind Measurement as Modeling Input Wind Load Analysis for Drivetrain Reliability TurbineMeteorological Tower
  31. 31. © 2016 Sentient Science Corporation – Confidential & Proprietary Poll Question Wind Load Analysis for Drivetrain Reliability
  32. 32. © 2016 Sentient Science Corporation – Confidential & Proprietary Turbine Load History • To understand what loads a turbine experienced over its lifetime, we need to simulate its complete history • The history can contain of several years • Simulating its complete history with a full dynamic model is almost impossible • For that reason, we convert our Turbine Model into a Reduced Order Model (ROM) Wind Load Analysis for Drivetrain Reliability
  33. 33. © 2016 Sentient Science Corporation – Confidential & Proprietary Turbine System Modeling of Dynamic Events Wind Load Analysis for Drivetrain Reliability 0 5 10 15 20 0 10 20 30 40 RotorSpeed[rpm] Time [s] (source: https://youtu.be/dvrttCzpUh4)
  34. 34. © 2016 Sentient Science Corporation – Confidential & Proprietary Turbine System Modeling of Dynamic Events Wind Load Analysis for Drivetrain Reliability
  35. 35. © 2016 Sentient Science Corporation – Confidential & Proprietary Summary • Accurate life prediction of wind turbine components requires detailed turbine- level modeling • Sentient performs aeroelastic modeling of wind turbines to extract loads on critical components • Sentient’s approach utilizes reverse engineering to determine the properties of the full turbine, including the blades and the controller • Turbine model is wrapped within a reduced order modeling framework to provide scalable, fast, individualized predictions of turbine component loads and life • SCADA-based wind conditions are used as an input to the reduced order model, having been validated for accuracy using uptower sensor and meteorological tower measurements Wind Load Analysis for Drivetrain Reliability
  36. 36. © 2016 Sentient Science Corporation – Confidential & Proprietary Models Applications Wind Load Analysis for Drivetrain Reliability Component Type Uprate/ Derate Supply Chain Forecasting Operation & Maintenance Planning Turbine Analysis for Repowering Asset Life Prediction
  37. 37. © 2016 Sentient Science Corporation – Confidential & Proprietary Thank you Meet the Team! Upcoming White Paper: 1. Seasonal effect of the load distribution of a wind turbine – May 2017 2. True fatigue load analysis based on SCADA – May 2017 Upcoming Webinars: 1. Moventas GE 1. Extra Life Gearbox Achieves 4x Life - May 9th 8AM EST & 1PM EST Wind Load Analysis for Drivetrain Reliability
  38. 38. © 2016 Sentient Science Corporation – Confidential & Proprietary Questions? Natalie Hils Director, Revenue Marketing nhils@sentientscience.com +1 716.807.8655 Dr. Adrijan Ribaric Vice President, Systems Technology aribaric@sentientscience.com Wind Load Analysis for Drivetrain Reliability

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