Data Acquisition, System Identification and Model Updating of an In-Service 2.5 MW Wind Turbine

Öz

The importance of wind energy is increasing day by day due to the rapidly increasing energy needs around the world and the necessity of meeting these needs with renawable solutions. Relatively large wind turbines, which can produce more energy in a short time, are also built in earthquake zones due to high wind energy potential. In order for wind turbines to remain operational throughout their economic life, it is important to monitor their structural integrity and to determine their dynamic (modal) properties under different operational and environmental conditions. In this study, a novel data acquisition system is designed for a wind turbine with a production capacity of 2.5 MW. In addition to real-time acquisition of acceleration, temperature and humidity data collected by the novel system, wind speed, wind direction, rotor speed, nacelle direction, pitch angle and instantaneous energy production data obtained from the turbine SCADA system are recorded and merged wıth the other data synchronously. Using the acceleration data collected under different environmental and operational conditions, numerous operational modal analysis are performed, and the dynamic modal properties of the turbine tower are estimated and a correlative work between the estimated modal parameters with environmetal and operational factors are presented. Finally, the numerical model of the turbine is updated using the estimated values by a Bayesian method.

Anahtar Kelimeler

• Model Updating
• System Identification
• Operational Modal Analysis
• Operational and Enviromental Factors
• Wind Turbines

Kullanımda Olan 2,5 MW Kapasiteli Bir Rüzgâr Türbininden Veri Toplanması, Sistem Tanımlama ve Model Güncelleme

Öz

Rüzgâr enerjisi, dünya genelinde hızla artan enerji ihtiyacı ve bu ihtiyacın çevreci çözümlerle giderilmesi gerekliliğiyle önemini her geçen gün arttırmaktadır. Kısa zamanda daha çok enerji üretebilen görece büyük rüzgâr türbinleri, rüzgâr enerji potansiyelinin fazla olması nedeniyle deprem bölgelerinde de inşa edilmektedir. Rüzgâr türbinlerinin ekonomik ömrü boyunca operasyonel kalabilmesi için yapısal bütünlüğünün izlenmesi ve dinamik özelliklerinin değişken operasyonel ve çevresel faktörler altında belirlenmesi büyük önem taşımaktadır. Bu çalışmada, kullanımda olan 2,5 MW üretim kapasiteli bir rüzgâr türbini için özgün veri toplama sistemi tasarlanmıştır. Sistemin kendi sensörlerinin topladığı ivme, sıcaklık ve nem verilerine ek olarak türbin SCADA sisteminden alınan rüzgâr hızı, rüzgâr yönü, rotor hızı, nasel yönü, pitch açısı ve anlık enerji üretim değeri verileri senkronize olarak kaydedilmiştir. Farklı çevresel ve operasyonel koşullar altında toplanan ivme verileri ile operasyonel modal analizler yapılmış ve türbinin dinamik özellikleri belirlenmiştir. Son olarak mod frekanslarının çevresel ve operasyonel faktörler ile ilişkisi de göz önünde bulundurularak, türbinin sayısal modeli güncellenmiştir.

Anahtar Kelimeler

• Model Güncelleme
• Sistem Tanımlama
• Operasyonel Modal Analiz
• Operasyonel ve Çevresel Etkiler
• Rüzgar Türbinleri

Kaynakça

1. [1] World Wind Energy Association. 2022. World Market for Wind Power Saw Another Record Year in 2021. https://wwindea.org/world-market-for-wind-power-saw-another-record-year-in-2021-973-gigawatt-of-new-capacity-added/ (Erişim Tarihi: 10.09.2022).
2. [2] Türkiye Rüzgâr Enerjisi Santralleri Birliği. 2022. Türkiye Rüzgâr Enerjisi İstatistik Raporu Ocak 2022.
3. [3] T.C. Enerji ve Tabii Kaynaklar Bakanlığı. 2022. Türkiye Rüzgâr Enerjisi Kapasite Faktörü Dağılımı Haritası. https://repa.enerji.gov.tr/REPA/bolgeler/TURKIYE-GENELI.pdf (Erişim tarihi: 10.09.2022).
4. [4] Afet ve Acil Durum Yönetimi Başkanlığı (AFAD). 2018. Türkiye Deprem Tehlike Haritası. https://deprem.afad.gov.tr/deprem-tehlike-haritasi (Erişim tarihi: 10.09.2022).
5. [5] Tcherniak, D., Chauhan, S., Hansen, M.H. 2010. Applicability Limits of Operational Modal Analysis to Operational Wind Turbines. IMAC-XXVIII February 1-4 Şubat, Jacksonville, USA.
6. [6] Tcherniak D., Allen, M.S. 2015. Experimental characterization of an operating Vestas V27 wind turbine using harmonic power spectra and OMA SSI. 6th International Operational Modal Analysis Conference 12-14 Mayıs, Gijon, Spain.
7. [7] Magalhães, F., & Cunha, Á. 2011. Explaining operational modal analysis with data from an arch bridge. Mechanical systems and signal processing, Cilt 25(5), s. 1431-1450. DOI: http://dx.doi.org/10.1016/j.ymssp.2010.08.001
8. [8] Kim, H. C., Kim, M. H., Choe, D. E. 2019. Structural health monitoring of towers and blades for floating offshore wind turbines using operational modal analysis and modal properties with numerical-sensor signals. Ocean Engineering, Cilt 188. DOI: https://doi.org/10.1016/j.oceaneng.2019.106226

9. [9] Zhao, Y., Zhang, Y., Xu, J., Yu, P., Sun, B. 2022. Shaking table test on seismic performance of integrated station-bridge high-speed railway station, Structures. Cilt 46, s. 1981-1993. DOI: https://doi.org/10.1016/j.istruc.2022.11.042
10. [10] Pacheco-Chérrez, J., & Probst, O. 2022. Vibration-based damage detection in a wind turbine blade through operational modal analysis under wind excitation. Materials Today: Proceedings, Cilt 56, s. 291-297. DOI: https://doi.org/10.1016/j.matpr.2022.01.159
11. [11] Saidin, S. S., Kudus, S. A., Jamadin, A., Anuar, M. A., Amin, N. M., Ibrahim, Z., ... & Sugiura, K. 2022. Operational modal analysis and finite element model updating of ultra-high-performance concrete bridge based on ambient vibration test. Case Studies in Construction Materials, Cilt 16. DOI: https://doi.org/10.1016/j.cscm.2022.e01117
12. [12] Avci, O., Alkhamis, K., Abdeljaber, O., Alsharo, A., & Hussein, M. 2022. Operational modal analysis and finite element model updating of a 230 m tall tower. Structures Cilt 37, s. 154-167. DOI: https://doi.org/10.1016/j.istruc.2021.12.078
13. [13] Li, J., Bao, T., & Ventura, C. E. 2022. An automated operational modal analysis algorithm and its application to concrete dams. Mechanical Systems and Signal Processing, Cilt 168. DOI: https://doi.org/10.1016/j.ymssp.2021.108707
14. [14] Hu, W. H., Thöns, S., Rohrmann, R. G., Said, S., Rücker, W. 2015. Vibration-based structural health monitoring of a wind turbine system. Part I: Resonance phenomenon, Engineering Structures, Cilt 89, s. 260-272. DOI: http://dx.doi.org/10.1016/j.engstruct.2014.12.034
15. [15] Hu, W. H., Thöns, S., Rohrmann, R. G., Said, S., Rücker, W. 2015. Vibration-based structural health monitoring of a wind turbine system Part II: Environmental/operational effects on dynamic properties, Engineering Structures, Cilt 89, s. 273-290. DOI: http://dx.doi.org/10.1016/j.engstruct.2014.12.035
16. [16] Worden, K., Tomlinson, G. R. 2019. Nonlinearity in structural dynamics: detection, identification and modelling. CRC Press.
17. [17] Beck, J. L., & Katafygiotis, L. S. 1998. Updating models and their uncertainties. I: Bayesian statistical framework, Journal of Engineering Mechanics-Proceedings of the ASCE, Cilt 124(4), s. 455-462. DOI: https://doi.org/10.1061/(asce)0733-9399(1998)124:4(455)
18. [18] Katafygiotis, L. S., & Beck, J. L. 1998. Updating models and their uncertainties. II: Model identifiability, Journal of Engineering Mechanics, Cilt 124(4), s. 463-467. DOI: https://doi.org/10.1061/(asce)0733-9399(1998)124:4(463)
19. [19] Katafygiotis, L. S., Papadimitriou, C., Lam, H. F. 1998. A probabilistic approach to structural model updating, Soil Dynamics and Earthquake Engineering, Cilt 17(7-8), s. 495-507. DOI: https://doi.org/10.1016/S0267-7261(98)00008-6
20. [20] Lam, H. F., Katafygiotis, L. S., & Mickleborough, N. C. 2004. Application of a statistical model updating approach on phase I of the IASC-ASCE structural health monitoring benchmark study, Journal of engineering mechanics, Cilt 130(1), s. 34-48. DOI: https://doi.org/10.1061/(asce)0733-9399(2004)130:1(34)
21. [21] Au, S. K. 2012. Connecting Bayesian and frequentist quantification of parameter uncertainty in system identification, Mechanical systems and signal processing, Cilt 29, s. 328-342. DOI: https://doi.org/10.1016/j.ymssp.2012.01.010
22. [22] Giagopoulos, D., Arailopoulos, A., Dertimanis, V., Papadimitriou, C., Chatzi, E., Grompanopoulos, K. 2019. Structural health monitoring and fatigue damage estimation using vibration measurements and finite element model updating, Structural Health Monitoring, Cilt 18(4), s. 1189-1206. DOI: https://doi.org/10.1177/1475921718790188
23. [23] Ching, J., Beck, J. L. 2004. New Bayesian model updating algorithm applied to a structural health monitoring benchmark, Structural Health Monitoring, Cilt 3(4), s. 313-332. DOI: https://doi.org/10.1177/1475921704047499
24. [24] Yuen, K. V., Beck, J. L., Katafygiotis, L. S. 2006. Efficient model updating and health monitoring methodology using incomplete modal data without mode matching, Structural Control and Health Monitoring: The Official Journal of the International Association for Structural Control and Monitoring and of the European Association for the Control of Structures, Cilt 13(1), s. 91-107. DOI: https://doi.org/10.1002/stc.144
25. [25] Azam, S. E., Papadimitriou, C., Chatzi, E. 2014. Recursive Bayesian filtering for displacement estimation via output-only vibration measurements, In Proceedings of the 2014 World Congress on Advances in Civil, Environmental, and Materials Research.
26. [26] Mottershead, J. E., & Friswell, M. I. 1993. Model updating in structural dynamics: a survey. Journal of sound and vibration, Cilt 167(2), s. 347-375. DOI: http://dx.doi.org/10.1006/jsvi.1993.1340
27. [27] Ali, A., De Risi, R., Sextos, A. 2021. Seismic assessment of wind turbines: How crucial is rotor-nacelle-assembly numerical modeling?, Soil Dynamics and Earthquake Engineering, Cilt 141
28. [28] ANSYS 2019 R3 Issued by ANSYS, Inc., Southpointe 2600 ANSYS Drive Canonsburg, PA 15317, USA.
29. [29] LabVIEW 2020 Professional development system by National Instruments. 11500 North Mopac Austin, Texas 78759 USA.
30. [30] ARTeMIS Modal Pro 4.0.1.5 2016 Ambient response testing and modal identification software by Structural Vibration Solutions ApS. NOVI Science Park, Niels Jernes Vej 10, DK 9220 Aalborg East, Denmark. [31] Brincker, R., Zhang, L., Andersen, P. 2001. Modal identification of output-only systems using frequency domain decomposition. Smart materials and structures, Cilt 10(3), s. 441.
31. [32] Allemang, R. J. 2003. The modal assurance criterion-twenty years of use and abuse. Sound and Vibration, Cilt 37 (8), 14-23.