PREDICTIVE ENGINEERING IN WIND ENERGY: A DATA-MINING APPROACH

1. PREDICTIVE ENGINEERING IN WIND ENERGY: A DATA-MINING APPROACH Student: Wenyan (Emily) Li PI: Andrew Kusiak Feb.12, 2010

2. Outline • Predictive Engineering in Wind Energy: Data-driven Approach Overview • Case Study • Short Term Prediction of Wind Turbine Parameters • Dynamic Control of Wind Turbines • Current Challenge • Prediction and Diagnosis of Wind Turbine Faults

3. Predictive Engineering in Wind Energy: Data-driven Approach Overview • Main Topics • Data Description

4. Main Topics • Existing approaches primarily use statistical methods. • Data mining approach: • Power Prediction and Optimization • Wind Speed Forecasting • Control of Wind Turbines • Vibration Analysis of Wind Turbine Components • Condition Monitoring and Fault Diagnosis

5. Data Description • The data used in the research was generated at a wind farm with about 100 turbines. • The data was collected by a SCADA (Supervisory Control and Data Acquisition) system installed at each wind turbine. • Each SCADA system collects data for more than 120 parameters. • Controllable parameters, e.g., blade pitch angle, generator torque • Non-controllable parameters, e.g., wind speed • Turbine performance parameters, e.g., power output, rotor speed. • Though the data is sampled at a high frequency, it is normally averaged and stored at a lower frequency, such as 10-minute intervals or 10-second intervals.

6. Data Description

7. Data Description

8. Basics • Physics-based equations: Table 6. Parameter selection for clustering in wind power prediction

9. Outline • Predictive Engineering in Wind Energy: Data-driven Approach Overview • Case Study • Short Term Prediction of Wind Turbine Parameters • Dynamic Control of Wind Turbines • Current Challenge • Prediction and Diagnosis of Wind Turbine Faults

10. Short Term Prediction of Wind Turbine Parameters • Background • Methodology • Feature Selection • Data Sampling • Model Construction • Computational Results

11. Background • Considering the fact that wind speeds and wind turbine performance vary across different turbine locations of a wind farm, the question arises as to whether a generalized model (called here a virtual model) of a wind turbine could be developed. • Such a virtual model has been developed based on SCADA data collected at wind turbines.

12. Methodology Methodology for developing virtual models

13. Data Collection: An Ideal Power Curve Ideal power curve

14. Data Collection: Actual Power Curve Actual wind speed and power curve

15. Parameter Selection The process of parameter selection Selection method: Combined domain knowledge and data mining algorithms

16. Data Sampling Figure 11. Illustration of data sampling. As the wind speed in the interval [3.5-13] m/s is studied, 1500 data points were randomly selected from low wind speed data set in each category of the wind speed data to form a training data.

17. Model Building Model extraction using different algorithms

18. Computational Results Prediction results of power output Prediction results of rotor speed

19. Outline • Predictive Engineering in Wind Energy: Data-driven Approach Overview • Case Study • Short Term Prediction of Wind Turbine Parameters • Dynamic Control of Wind Turbines • Current Challenge • Prediction and Diagnosis of Wind Turbine Faults

20. Dynamic Control of Wind Turbines • Background • Dynamic Model • Adjusting Objectives based on Wind Conditions and Operational Requirements • Computational Results • Illustration of Operational Scenarios

21. Background • Most optimization problems aim to maximize power output of wind turbine; • In reality, there are a number of optimal objectives; • The model considered in this paper considers five weighted objectives. The weights are adjusted according to eight typical scenarios defined by wind conditions and operational requirements.

22. Dynamic Model is a function to minimize the distance between the power output to its upper limit and therefore maximizing the power output; is a function to minimize rotor speed ramp; is a function to minimize power output ramp; is a function to minimize generation torque ramp; is a function to minimize pitch angle ramp;

23. Adjusting Objectives based on Wind Conditions and Operational Requirements • Turbulence Intensity • is used as the threshold to distinguish between high turbulence intensity and low turbulence intensity • Wind Speed • The speed of is used as an threshold to distinguish between high wind speed and low wind speed • Electricity demand • High and low electricity demand (arbitrarily)

24. Adjusting Objectives based on Wind Conditions and Operational Requirements Weights (arbitrarily) distribution for eight scenarios Adjust weights according to the scenario category

25. Computational Results Summary of power output generation

26. Computational Results Summary of power output generation

27. Operational Scenario 4: High Wind Speed, Low Turbulence Intensity, and Low Electricity Demand Optimal results for operational scenario 4

28. Operational Scenario 7: Low Wind Speed, Low Turbulence Intensity, and High Electricity Demand Optimal results for operational scenario 7

29. Outline • Predictive Engineering in Wind Energy: Data-driven Approach Overview • Case Study • Short Term Prediction of Wind Turbine Parameters • Dynamic Control of Wind Turbines • Current Challenge • Prediction and Diagnosis of Wind Turbine Faults

30. Prediction and Diagnosis of Wind Turbine Faults • Background • Data Description • Current Methodology • Current Challenge • Solution?

31. Introduction • The expansion of wind power has increased interest in operations and maintenance of wind turbines. • The operations, maintenance, and part replacement costs are expensive when wind turbine or its components break down. • Condition monitoring and fault diagnosis of wind turbines are of high priority.

32. Data Description • Two separate data files: • Original SCADA data at 5-min intervals; • Status/Fault data is provided when the fault occurs; Parameters related to the fault information

33. Data Description • Fault distribution of a random turbine Fault distribution for a random turbine There are 35 specific faults (11 in category 1 + 20 in category 2 + 4 in category 3) and 31 different status occurrences. In total 233 (42 + 131 + 60) faults and 1096 statuses are captured during the three month period.

34. Current Method • Three-level fault prediction • General process for each level: Levels for fault prediction Process of fault prediction

35. Current Challenge • Limited fault data • Data sampling is not representative • Difficult to distinguish fault category • Difficult to predict specific fault

36. Solution?