Blade Load Estimations by a Load Database for an Implementation in SCADA Systems

1. Blade Load Estimations by a Load Database for an Implementation in SCADA Systems Master Thesis Presentation Carlos Ochoa A. TUDidnr. 4145658 TU/e idnr. 0756832 October 23Th, 2012

2. CONTENTS • Introduction • Objective • OWEZ Data • Method • Load Comparison Between Turbines • Load Database Construction • Database Estimators Validation • Conclusions Blade Load Estimations by Database for SCADA

3. 1. Introduction • Real Wind Conditions Z • Different inflow parameters affect the turbine behavior, factors as: • Wind Speed • Wind Shear • Turbulence • Atmospheric stability • etc. • All these parameters have an impact over the forces and moments of the turbine. Turbulence Wind Speed  Occurrences FT(V,u,z)  FG MY(Ω) Y Ω Q(V) X FC Blade Load Estimations by Database for SCADA

4. 1. Introduction • Real Wind Conditions • Loads and Fatigue The cyclic loads affects the fatigue in the materials, this limits the lifetime of a wind turbine. In a wind turbine, the blades are structural components that have the largest provability of failureafter determinate period. Blade Load Estimations by Database for SCADA

5. 1. Introduction • Real Wind Conditions • Fatigue • SCADA • Collect, monitor & storage of turbine behavior through the Standards Signals: • Generator rotational speed and acceleration • Electrical power output. • Pitch angle. • Lateral and longitudinal tower top acceleration. • Wind Speed and wind direction. • Only the main Statistics of the selected variables are computed. • Min, max, average & standard deviation. Blade Load Estimations by Database for SCADA

6. 2. Objectives Develop a method to estimate the blade load behavior by retrieving information from a measurement database depending on the standard signals of the wind turbine, which are usually stored by the SCADA system. How accurate are the fatigue damages and the cumulative fatigue estimations when comparing them against other load estimation methods results? Neural Networks Regression Techniques Blade Load Estimations by Database for SCADA

7. 3. OWEZ Data High frequency measurement data (32Hz) from two turbines were obtained trough a measuring campaign at OWEZ. 41 different signals were measured for each different turbine for several months. • Key Signals Measured (32Hz): • Stain signals from the root of the blade • Edgewise • Flapwise • Other 70 signals • Standard signals • Standard Reconstruction of SCADA data Blade Load Estimations by Database for SCADA

8. 4. Method The data was classified depending on the turbine, mean winds speed and turbulence intensity. Under each wind inflow condition different load behavior is produced. From these, Rainflow counting matrixes and load amplitudes histograms are obtained. From the load amplitude histograms, load estimators can be derived. The groups of estimators are storage on a database. Load time Series Rainflow Counting Matrixes Load Amplitude Histograms Load Distribution Functions To perform a load estimation, the elements of the database can be retrieved by the use of the SCADAdata. Load Estimators Blade Load Estimations by Database for SCADA

9. 4. Method Blade Load Estimations by Database for SCADA

10. 4. Method To convert the Rainflow cycle matrixes to load histograms certain material characteristics were assumed. The geometry of the blade root (thickness and chord) was estimated. A linear Goodman diagram was obtained from the use of the assumed blade characteristics. By its use, load cycle histograms were obtained. Blade Load Estimations by Database for SCADA

11. 5. Load Comparison Between Turbines From the OWEZ data, the load patterns from both turbines were compared. From all the wind conditions, the comparison results shown a remarkable similitude between loads. • Turbine 8  • Turbine 7  Blade Load Estimations by Database for SCADA

12. 6. Load Database Construction All the inflow condition measured were processed to obtain the load database. Interesting patternscame up when analyzing the changesof the load behavior trough the wind speed. Especially in the edgewisedirection. Blade Load Estimations by Database for SCADA

13. 6. Load Database Construction In contrast, other patterns came up when analyzing the load behavior changes trough the turbulence intensity. Edgewise • Mean Wind Speed 7m/s. • Turbulence Intensity: • 9% • 11% • 13% • 15% • 17% Flapwise Blade Load Estimations by Database for SCADA

14. 6. Load Database Construction From all the load histograms generated, load distributions functions were constructed; all these were normalized to 10-min. All the load distribution functions were made by piecewise functions, for the edgewise case three polynomials were used. For the flapwise functions only two functions were used. To fit better the tail behavior, a moving average with a ratio of 1:5 was used . The tails were fitted with a linear or a quadratic function in the logarithmic scale. Blade Load Estimations by Database for SCADA

15. 6. Load Database Construction Respect to the idling condition, it was characterized only for all the speeds lower the cut-in wind speed. It was interesting to note the apparent gravity peak pattern seen in the flapwise direction. The same gravity peakappear at power production cases with low winds speeds. It is caused by the high pitching angles of the idling conditions. In the edgewise direction, it causes the appearance of a double peak. Blade Load Estimations by Database for SCADA

16. 6. Load Database Construction From all the load distribution functions load estimators can be derived; they can take form as equivalent loads, fatigue damages or even maximum load values were obtained. The next are examples from the fatigue damages normalized for 10-min. Linear fatigue damage increase with the turbulence intensity for the edgewise direction, exponential for flapwise. Blade Load Estimations by Database for SCADA

17. 7. Database Estimators Validation When comparing a single random 10-min. load sequence with the loaddistributions from the database, it was observed they does not match well. Scatter appears especially at the tail of the edgewise distribution. Furthermore, it was noticed the histogram data points show spaces between bin counts. Not every 5KNm in the cycle load amplitude axis has a count. Blade Load Estimations by Database for SCADA

18. 7. Database Estimators Validation From the database constructed is possible to estimate the cumulative fatigue of such turbine. It can be estimated with the database information and compared with the sum of all the 10-min. calculated fatigue damages. From: 200-300 KNm 11/20 counts From: 650 -700 KNm 7/10 counts The error range from 31.4% and 41%. They can be attributed to the scatter and the missed counts trough each single load histogram. Blade Load Estimations by Database for SCADA

19. 7. Database Estimators Validation It was possible to improve the cumulative fatigue estimation by the use of a multiplication constant. The main idea was not to fix the final value of the estimation with the calculation result, but to make the slope of this line as similar as possible to the calculation line. The multiplication constant obtained was 0.835. With this, the errors diminished to 10.7% and 15%. Using the database from the turbine 7 data and its correction, the cumulative fatigue of the turbine 8 was estimated and its errors range from 9.44 to 10.3% Blade Load Estimations by Database for SCADA

20. 7. Database Estimators Validation From the database made with the turbine 7 another turbine cumulative fatigue was estimated. Blade Load Estimations by Database for SCADA

21. 7. Database Estimators Validation For the previous results, all the single fatigue estimation were retrieved from the load database by means of the reconstructed SCADA data. For this, the pitching angle information is extremely useful to identify the turbine status. The main statistical values of the wind speed where used as well. Wind Direction In real life applications, other variablesfrom the SCADA data, as the electrical power output or the generator speed, could be used to corroborate the turbine status. Power Production Pitch Angle: 0-25° Idling Pitch Angle: 25-40° The load estimators do not necessarily have to be retrieved from the database each 10-min. This period can be fixed by the frequency the SCADA system update its variables. Start Up Pitch Angle: ～ 45° Pause, Stop & E. Stop Pitch Angle: ～ 90° Blade Load Estimations by Database for SCADA

22. 8. Conclusions • It was possible to create a load estimation method based on previous turbine measurements and on SCADA data information. • The fatigue accumulation estimations from both turbines give back smaller errors than other methodologies. The errors range from 9 to 15%. • Estimations by neural networks produce errors ranging from 12 to 22% depending on the number of nodes used in the network. • Regression techniques have errors ranging from 2 to 23%. • Nevertheless, the methodology proposed in this report still needs to be validated by more turbines. • Given the similar load patterns obtained from different turbines under the same wind conditions, the method developed could be applied to other couple of turbines. • Thanks to the cumulative loading estimation of the turbine blades, would help to determine wheatear or not to extend the turbine service lifetimeormodify the turbine maintenance program, this could mean to be a significant monetary advantage. Blade Load Estimations by Database for SCADA

23. Thanks for the Attention Questions…? Blade Load Estimations by Database for SCADA Esbjerg, Support Structure Design Esbjerg, Support Structure Design The New York–Long Island 340MW Project