1. 12-5-2015 Challenge the future Delft University of Technology Blade Load Estimations by a Load Database for an Implementation in SCADA Systems Master Thesis Presentation Carlos Ochoa A. TUD idnr. 4145658 TU/e idnr. 0756832 October 23 Th, 2012

2. 2 SET MSc – Wind Energy CONTENTS 1.Introduction 2.Objective 3.OWEZ Data 4.Method 5.Load Comparison Between Turbines 6.Load Database Construction 7.Database Estimators Validation 8.Conclusions Blade Load Estimations by Database for SCADA

3. 3 SET MSc – Wind Energy Z Y X 1. Introduction  F T (V,u,z) FCFC FGFG Ω Q (V)  MY(Ω)MY(Ω) Real Wind Conditions Blade Load Estimations by Database for SCADA Occurrences Turbulence Wind Speed 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.

4. 4 SET MSc – Wind Energy Blade Load Estimations by Database for SCADA 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 failure after determinate period. 1. Introduction

5. 5 SET MSc – Wind Energy Blade Load Estimations by Database for SCADA 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. 1. Introduction Only the main Statistics of the selected variables are computed. Min, max, average & standard deviation.

6. 6 SET MSc – Wind Energy 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. Blade Load Estimations by Database for SCADA How accurate are the fatigue damages and the cumulative fatigue estimations when comparing them against other load estimation methods results? Neural Networks Regression Techniques

7. 7 SET MSc – Wind Energy 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. Blade Load Estimations by Database for SCADA Key Signals Measured (32Hz): Stain signals from the root of the blade Edgewise Flapwise Other 70 signals Standard signals Standard Reconstruction of SCADA data

8. 8 SET MSc – Wind Energy 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. Blade Load Estimations by Database for SCADA From the load amplitude histograms, load estimators can be derived. The groups of estimators are storage on a database. To perform a load estimation, the elements of the database can be retrieved by the use of the SCADA data. Load time Series Rainflow Counting Matrixes Load Amplitude Histograms Load Distribution Functions Load Estimators

9. 9 SET MSc – Wind Energy 4. Method Blade Load Estimations by Database for SCADA

10. 10 SET MSc – Wind Energy 4. Method Blade Load Estimations by Database for SCADA 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.

11. 11 SET MSc – Wind Energy 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. Blade Load Estimations by Database for SCADA Turbine 8 Turbine 7 5. Load Comparison Between Turbines

12. 12 SET MSc – Wind Energy 6. Load Database Construction Blade Load Estimations by Database for SCADA All the inflow condition measured were processed to obtain the load database. Interesting patterns came up when analyzing the changes of the load behavior trough the wind speed. Especially in the edgewise direction.

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

14. 14 SET MSc – Wind Energy 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. Blade Load Estimations by Database for SCADA 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.

15. 15 SET MSc – Wind Energy 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. Blade Load Estimations by Database for SCADA The same gravity peak appear 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.

16. 16 SET MSc – Wind Energy 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. Blade Load Estimations by Database for SCADA Linear fatigue damage increase with the turbulence intensity for the edgewise direction, exponential for flapwise.

17. 17 SET MSc – Wind Energy 7. Database Estimators Validation When comparing a single random 10-min. load sequence with the load distributions from the database, it was observed they does not match well. Scatter appears especially at the tail of the edgewise distribution. Blade Load Estimations by Database for SCADA 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.

18. 18 SET MSc – Wind Energy 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. Blade Load Estimations by Database for SCADA The error range from 31.4% and 41%. They can be attributed to the scatter and the missed counts trough each single load histogram. From: 650 -700 KNm 7/10 counts From: 200-300 KNm 11/20 counts

19. 19 SET MSc – Wind Energy 7. Database Estimators Validation Blade Load Estimations by Database for SCADA 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%

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

21. 21 SET MSc – Wind Energy 7. Database Estimators Validation Blade Load Estimations by Database for SCADA 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 Power Production Pitch Angle: 0-25° Start Up Pitch Angle: ～ 45° Pause, Stop & E. Stop Pitch Angle: ～ 90° Idling Pitch Angle: 25-40° In real life applications, other variables from the SCADA data, as the electrical power output or the generator speed, could be used to corroborate the turbine status. 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.

22. 22 SET MSc – Wind Energy 8. Conclusions Blade Load Estimations by Database for SCADA 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 lifetime or modify the turbine maintenance program, this could mean to be a significant monetary advantage.

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