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Diagnosis of wind turbine misalignment through SCADA data

Autorzy Astolfi, D.  Castellani, F.  Scappaticci, L.  Terzi, L. 
Treść / Zawartość
Warianty tytułu
Języki publikacji EN
EN Optimal alignment of wind turbines to the wind direction is a crucial condition for the quality of power output and for the health of the turbines. Actually, bad alignment can cause degraded performances and dangerous loads that can affect, on the long run, the mechanical safety of the wind turbine. Supervisory Control And Data Acquisition (SCADA) systems are becoming widespread in modern wind energy technology because of the appreciable costs – benefits ratio. The common time scale of SCADA, yet, usually is not effective for misalignment diagnosis because the wind varies too rapidly. For this reason, misalignment is often diagnosed using ad hoc techniques as LIDAR-based or spinner anemometers. In the present work, it is shown that very useful indications for the diagnosis of misalignment can be obtained also from the SCADA data, without invoking expensive supplementary control techniques. The method is validated on the data set of a wind farm sited in Italy.
Słowa kluczowe
PL energia wiatru   turbina wiatrowa   SCADA   system kontroli   diagnostyka uszkodzeń  
EN wind energy   wind turbine   SCADA system   fault diagnosis  
Wydawca Polskie Towarzystwo Diagnostyki Technicznej
Czasopismo Diagnostyka
Rocznik 2017
Tom Vol. 18, No. 1
Strony 17--24
Opis fizyczny Bibliogr. 45 poz., rys., tab., wykr.
autor Astolfi, D.
autor Castellani, F.
autor Scappaticci, L.
autor Terzi, L.
1. Castellani F, Astolfi D, Mana M, Burlando M, Meißner C, Piccioni E. Wind Power Forecasting techniques in complex terrain: ANN vs. ANN-CFD hybrid approach. Journal of Physics: Conference Series, 2014, 753(8): 082002.
2. Jónsson T, Pinson P, Madsen H. On the market impact of wind energy forecasts. Energy Economics, 2010; 32(2): 313-320.
3. Cassola F, Burlando M. Wind speed and wind energy forecast through Kalman filtering of Numerical Weather Prediction model output. Applied Energy, 2012; 99;154-166.
4. Castellani F, Burlando M, Taghizaded S, Astolfi D, Piccioni E. Wind energy forecast in complex sites with a hybrid neural network and CFD based method. Energy Procedia, 2014; 45:188-197.
5. Luo HY, Liu TQ, Li XY. Chaotic forecasting method of short-term wind speed in wind farm [J]. Power System Technology, 2009; 9:19.
6. Chen P, Chen H, Ye R. Chaotic wind speed series forecasting based on wavelet packet decomposition and support vector regression. In IPEC, 2010 Conference Proceedings., 2010: 256-261.
7. Tao D, Hongfei X. Wind speed chaotic prediction model based on optimal neighborhood. Acta Energiae Solaris Sinica, 2011; 4:21.
8. Xu X, Niu D, Fu M, Xia H, Wu H. A Multi Time Scale Wind Power Forecasting Model of a Chaotic Echo State Network Based on a Hybrid Algorithm of Particle Swarm Optimization and Tabu Search. Energies, 2015; 8(11):12388-12408.
9. Rodrigo JS, Gancarski P, Arroyo RC, Moriarty P, Chuchfield M, Naughton JW, Hansen KS, Machefaux E, Koblitz T, Maguire E. Iea-task 31 wakebench: Towards a protocol for wind farm flow model evaluation. part 1: Flow- over-terrain models. Journal of Physics: Conference Series, 2014; 524.
10. Moriarty P, Rodrigo JS, Gancarski P, Chuchfield M, Naughton JW, Hansen KS, Machefaux E, Maguire E, Castellani F, Terzi L. Iea-task 31 wakebench: Towards a protocol for wind farm flow model evaluation. part 2: Wind farm wake models. Journal of Physics: Conference Series, 2014; 524.
11. Castellani F, Astolfi D, Burlando M, Terzi L. Numerical modelling for wind farm operational assessment in complex terrain. Journal of Wind Engineering and Industrial Aerodynamics, 2015; 147:320-329.
12. Moreno P, Gravdahl AR, Romero M. Wind flow over complex terrain: application of linear and CFD models. European wind energy conference and exhibition, 2003: 16–19.
13. Lee M. Lee SH, Hur N, Choi CK. A numerical simulation of flow field in a wind farm on complex terrain. Wind and Structures, 2010: 13(4), 375.
14. Kusiak A, Song Z. Design of wind farm layout for maximum wind energy capture. Renewable Energy, 2010; 35(3): 685-694.
15. Wang L, Tan A, Gu Y. A novel control strategy approach to optimally design a wind farm layout. Renewable Energy, 2016; 95:10-21
16. Shakoor R, Hassan MY, Raheem A, Wu YK. Wake effect modeling: A review of wind farm layout optimization using Jensen׳ s model. Renewable and Sustainable Energy Reviews, 2016; 58: 1048-1059.
17. Castellani F, Gravdahl A, Crasto G, Piccioni E, Vignaroli A. A practical approach in the CFD simulation of off-shore wind farms through the actuator disc technique. Energy Procedia, 2013; 35:274-284.
18. Barthelmie R, Pryor S, Frandsen S, Hansen K, Schepers J, Rados K, Schlez W, Neubert A, Jensel L, Neckelmann S. Quantifying the impact of wind turbine wakes on power output at offshore wind farms. Journal of Atmospheric and Oceanic Technology, 20100; 27(8):1302-1317.
19. Porté-Agel F, Wu YT, Chen CH. A numerical study of the effects of wind direction on turbine wakes and power losses in a large wind farm. Energies, 2013; 6(10):5297–5313.
20. Castellani F, Astolfi D, Garinei A, Proietti S, Sdringola P, Terzi L, Desideri U. How wind turbines alignment to wind direction affects efficiency? A case study through SCADA data mining. Energy Procedia, 2015; 75: 697-703.
21. Castellani F, Astolfi D, Sdringola P, Proietti S, Terzi L. Analyzing wind turbine directional behavior: SCADA data mining techniques for efficiency and power assessment. Volume 185, Part 2, 1 January 2017, Pages 1076–1086
22. Gaumond M, Réthoré PE, Ott S, Pena A, Bechmann A, Hansen KS. Evaluation of the wind direction uncertainty and its impact on wake modeling at the horns rev offshore wind farm. Wind Energy, 2014; 17(8):1169–1178.
23. Wu YT, Porté - Agel F. Modeling turbine wakes and power losses within a wind farm using LES: An application to the Horns Rev offshore wind farm. Renewable Energy, 2015; 75:945-955.
24. Castellani F, Astolfi D, Piccioni E, Terzi L. Numerical and experimental methods for wake flow analysis in complex terrain. In Journal of Physics: Conference Series, 2105; 625(1): 012042.
25. Astolfi D, Castellani F, Garinei A, Terzi L. Data mining techniques for performance analysis of onshore wind farms. Applied Energy, 2015; 148: 220–233.
27. Castellani F, Garinei A, Terzi L, Astolfi D, Gaudiosi M. Improving windfarm operation practice through numerical modelling and supervisory control and data acquisition data analysis. Renewable Power Generation, 2014; 8(4):367-379
28. Herp J, Pedersen NL, Nadimi ES. Wind turbine performance analysis based on multivariate higher order moments and bayesian classifiers. Control Engineering Practice, 2016; 49:204-211.
29. Chang TP, Liu FJ, Ko HH, Cheng SP, Sun LC, Kuo SC. Comparative analysis on power curve models of wind turbine generator in estimating capacity factor. Energy, 2014; 73: 88-95.
30. Castellani F, Astolfi D, Terzi L, Hansen KS, Rodrigo JS. Analysing wind farm efficiency on complex terrains. Journal of Physics: Conference Series, Vol. 524, No. 1, pp. 012142.
31. Castellani F, D'Elia G, Astolfi D, Mucchi E, Dalpiaz G, Terzi L. Analyzing wind turbine flow interaction through vibration data. Journal of Physics: Conference Series. Vol. 753, No. 11, pp. 112008
32. Zaher ASAE, McArthur SDJ, Infield DG, Patel Y. Online wind turbine fault detection through automated SCADA data analysis. Wind Energy,2009; 12(6), 574-593.
33. Schlechtingen M, Santos IF, Achiche S. Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 1: System description. Applied Soft Computing, 2013; 13(1):259-270.
34. Schlechtingen M, Santos IF. Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 2: Application examples. Applied Soft Computing, 2014; 14, 447-460.
35. Kim K, Parthasarathy G, Uluyol O, Foslien W, Sheng S, Fleming P. Use of SCADA data for failure detection in wind turbines. In ASME 2011 5th International Conference on Energy Sustainability, 2011: 2071-2079).
36. Astolfi D, Castellani F, Terzi L. Mathematical methods for SCADA data mining of onshore wind farms: Performance evaluation and wake analysis. Wind Engineering. 2016; 40(1):69-85.
37. Condition Monitoring. Exploring the innovations, challenges and potential of the products and services that keep wind turbines operating. Windpower Monthly, Haymarket Media Group, July 2013.
38. Wind Turbine Control Systems. Exploring the capabilities of the latest systems, and the drivers and challenges for further development. Windpower Monthly, Haymarket Media Group, March 2014.
39. Smith DA, Harris M, Coffey AS, Mikkelsen T, Jørgensen HE, Mann J, Danielian R. Wind lidar evaluation at the Danish wind test site in Høvsøre. Wind Energy, 2006; 9(1-2): 87-93.
40. Schlipf D, Schlipf DJ, Kühn M. Nonlinear model predictive control of wind turbines using LIDAR. Wind Energy, 2013; 16(7):1107-1129.
41. Lang S, McKeogh E. LIDAR and SODAR measurements of wind speed and direction in upland terrain for wind energy purposes. Remote Sensing, 2011; 3(9):1871-1901.
42. Mikkelsen T, Angelou N, Hansen K, Sjöholm M., Harris M, Slinger C, Vives G. A spinner integrated wind lidar for enhanced wind turbine control. Wind Energy, 2013; 16(4): 625-643.
43. Zahle F, Sørensen NN. Characterization of the unsteady flow in the nacelle region of a modern wind turbine. Wind Energy, 2011: 14(2):271-283.
44. Demurtas G, Pedersen TF, Zahle F.Calibration of a spinner anemometer for wind speed measurements. Wind Energy. 2016; 19:2003–2021
45. Emanuel H, Schellschmidt M, Wachtel S, Adloff S. Power quality measurements of wind energy converters with full-scale converter according to IEC 61400-21. In Electrical Power Quality and Utilisation, 2009. EPQU 2009. 10th International Conference on (pp. 1-7). IEEE.
46. Braccesi C, Cianetti F, Tomassini L. An innovative modal approach for frequency domain stress recovery and fatigue damage evaluation. International Journal of Fatigue. 2016: 91, 382-396.
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
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