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BDE SC3.3 Workshop - Big Data in Wind Turbine Condition Monitoring


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Big Data in Wind Turbine Condition Monitoring (Prof. Jan Helsen) at the BigDataEurope Workshop, Amsterdam, Novermber 2017

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BDE SC3.3 Workshop - Big Data in Wind Turbine Condition Monitoring

  1. 1. Prof Jan Helsen Vrije Universiteit Brussel/OWI-lab Visiting Scholar MIT Leveraging physics with big-data 28.02.17 Amsterdam
  2. 2. § Set-up in 2010 as a new application lab coordinated by Sirris to support wind energy RD&I activities § Partnership with the 4 Flemish universities dealing with (offshore) wind energy research: VUB, UGent, Uantwerpen, KU Leuven § Range of unique test & monitoring infrastructures (large climate chamber / measurement equipment /…) § Focus on wind energy in harsh environments: offshore wind and cold climates § New cluster working (IBN-cluster) since 2017 Introduction OWI-Lab: Belgian RD&I center for wind energy Cluster & Platform support – Initiation of RD&I
  3. 3. Introduction OWI-Lab: Belgian RD&I center for wind energy Climatic test lab = Environmental testing of wind turbine components (Offshore) field testing & measurements DATA § CAPEX reduction § OPEX reduction § RISK reduction
  4. 4. Why big data? Image: NCTA • Exponential rise in the number of devices connected to internet • Wide coverage of high speed internet connections available • More and more embedded sensors at lower cost • Different sources of information are becoming available
  5. 5. Status Log Data Sensor Data Stream Status Log Data Sensor Data Stream Farm 1 Status Log Data Sensor Data Stream Status Log Data Sensor Data Stream Farm 2
  6. 6. Big Data Sources SCADA 1sec Status Codes Dedicated Measure- ments CMS SCADA 10min
  7. 7. Not just the numbers matter, the conclusion counts High Quality Actionable Data Data Stream Data Stream Data Stream Data Stream Data Stream Data Stream Data Stream Data Stream Analytics
  8. 8. Data Driven Design Validation
  9. 9. All Comes Down To Probability/Risk Of Failure
  10. 10. Need for deeper understanding of critical phenomena & Risk Minimization
  11. 11. Field-data enabled monitoring & design Re-design based on what can be learned from the information acquired during long-term measurements • What goes wrong(tonalities, failures)? • Its consequences • Its criticality Design based on simulation Model validation with laboratory tests Model validation with field tests Re-design based on long-term behavior F. Vanhollebeke. “Dynamic analysis of a wind turbine gearbox: Towards prediction of mechanical tonalities”. In: (2015). PhD thesis, K.U.Leuven. Gearbox Picture Reproduced from:
  12. 12. Turbine Reliability: Understand the failure Mode Grid Loss Event
  13. 13. Unveil The Fingerprint Of Failure/Flaws In Your Design
  14. 14. Introducti on • Failure modes extracted from failure data: • Example: NREL Failure database • HSS Bearings • WEC failures • Failure mode not completely understood • Slip is possible influencer Context
  15. 15. Introducti on • Bearing slip: • Roller slip • Cage slip • Widely believed to be playing important role in bearing failure Torque RPM ! Source: Timken Load Zone Acceleration + deceleration of rollers Context
  16. 16. Introducti on • Drivetrain torsional resonance: • Rotational motion of rotor and generator inertia • About drivetrain stiffness Context
  17. 17. • 750kW • Three point mounting • 1 Planetary Stage • 2 Helical Stages • Induction generator GRC Drivetrain on Dynamometer (DYNO) Test Object 1: Dyno
  18. 18. DYN O • Main shaft & HSS torque-bending • Speed at LSS and HSS • Strain gauges in HSS meshes • Strain gauges in HSS TRBs • Currents-Voltages at generator Instrumentation Test Object 1: Dyno
  19. 19. • GE 1.5MW • Three point mounting • 1 planetary stage • 2 helical gear stages • Doubly-fed induction generator Field Turbine (FIELD) Test Object 2: Field
  20. 20. • Main shaft torque-bending • Speed at LSS and HSS • Acceleros spread over gearbox housing • Currents-Voltages at generator Torque-RPM Signature Test Object 2: Field
  21. 21. Torque-RPM Signature Test Object 2: Field
  22. 22. Torque-RPM Signature Test Object 2: Field
  23. 23. • Emergency stop pitch rate determines negative torque behavior Influence Of Pitch Test Object 2: Field
  24. 24. Torque reversals govern dynamic part of the event Speed increase and decrease due to accelerations and decelerations linked to torque reversals Similar frequency content in response Torque-RPM Signature Comparison
  25. 25. • Gear impacts during torque reversals HSS Gear Mesh Test Object 1: Dyno
  26. 26. • Similarly constant torque periods à traveling through backlash à impacts present HSS Gear Mesh Test Object 2: Field
  27. 27. DYN O • Significant bending during negative torque periods • Decreasing effect at higher power levels HSS Bending Test Object 1: Dyno
  28. 28. DYN O • Bearing load distribution during event • For different power levels Positive torque HSS Bearing Forces Reproduced from GRC instrumentation design Test Object 1: Dyno
  29. 29. DYN O • Bearing load distribution during event • For different power levels Negative torque HSS Bearing Slip Reproduced from GRC instrumentation design Clear unloading Test Object 1: Dyno
  30. 30. DYN O • Frequency domain analysis combined with ordertracking for different power levels: negative torque periods HSS Bearing Slip Test Object 1: Dyno
  31. 31. • Impacts measured by acc at HSS • Potentially linked to changing loading • Conditions in HSS stage during reversal of torque HSS Axial Impacts Test Object 2: Field
  32. 32. Turbine Reliability: Condition Monitoring
  33. 33. Big Data Analytics Platform SCADA 1sec Status Codes Fully Automated CMS: Dataflow
  34. 34. 05-12-17 | 34 Streaming data
  35. 35. • Spectrogram calculated in streaming way • Continuous moving window • With overlap • 5120Hz data from drivetrain Streaming data
  36. 36. Streaming data
  37. 37. 37 Public domain case NREL GRC Condition Monitoring Round Robin
  38. 38. Machine Learning Methods Vibration Analysis Methods Bearing Temp 1 year
  39. 39. Complex Time-Intensive Calculation à Run Parallelized in Cloud BPFO of the planet carrier clearly visible
  40. 40. Turbine Reliability: Status Log Analysis In collaboration with U Antwerpen ADREM
  41. 41. • What do we want to learn? • What sequence of events lead to failure? • Comes down to: trigger followed by turbine response actions eventually resulting in failure • Detect this high level action sequence External Trigger Turbine Action 1 … Turbine Action n Failure Find those patterns interesting for failure detection
  42. 42. Each of these high-level actions comes down to a sequence of subactions External Trigger Turbine Action 1 … Example: Start-up RPM Increases Power Increases Blade Pitch Angle Constant Or Slightly Changing Sufficient Wind Speed Blade Pitch Angle Changes Significantly
  43. 43. Transaction database Sequence of items Sliding Windows Transform Frequent Itemset Mining (Eclat) with high support … 07:40 + 60m 08:07 + 60m 08:10 + 60m Frequent Itemsets …
  44. 44. Transaction database Select windows near alarm X Frequent Itemset Mining (Eclat) Frequent Itemsets near alarm X Projected Transaction database
  45. 45. • Use Pattern mining techniques to extract these episodes from the data • Use External Trigger to define search window
  46. 46. Turbine Event 1 Turbine Event 2 Turbine Event 1 Turbine Event 2
  47. 47. • Association rule best matching domain expert: • [Extremely High Wind, Speed Alert, ERROR Occurred, Paused State, Attempt to restart, rpm very low, pitch median, power very low] • Confidence of approx. 70% • Sequence of items not considered • Named “Stop after extremely high wind speed alarm” and added to level 1
  48. 48. Conclusions
  49. 49. Conclusions • Physics-based approaches using big data of added value to design and monitoring • Condition monitoring on long term data-sets for trend tracking • Vibration data augmented with temperature analysis • Status log pattern mining for detecting episodes in turbine event sequences
  50. 50. Thank you for your attention +32 479 85 58 79