nach oben

Erschienen in:

2018 | OriginalPaper | Buchkapitel

Big Data and Wind Turbines Maintenance Management

verfasst von : Alberto Pliego, Raúl Ruiz de la Hermosa, Fausto Pedro García Márquez

Erschienen in: Renewable Energies

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Nowadays, the modern technologies and processes demand a big amount of information in order to be optimised. As the consequence, a huge amount of data is being generated. This is the main cause of the current boom for the so called Big Data. There are a lot of systems and sensors capable of generating such data but the processing of these data is currently becoming an arduous task. This chapter is focused on the analysis of the Big Data associated with the maintenance of wind farms. An analysis of the data coming from Condition Monitoring and Supervisory Control and Data Acquisition Systems will be carried out. This analysis will be done using two methods whose objectives are to reduce the amount of data and, therefore, to facilitate the data processing. Two case studies will be presented in order to clarify how these methods should be applied.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Managing Costs and Review for Icing Problems

Nächstes Kapitel Societal and Environmental Impact of High Energy Return on Investment (EROI) Energy Access

Boyd D, Crawford K (2012) Critical questions for Big Data: provocations for a cultural, technological, and scholarly phenomenon. Inform Commun Soc 15(5):662–679CrossRef

Jara J (2012) Big Data & web intelligence

Ohlhorst F (2013) Turning Big Data into Big Money. Wiley, Hoboken, New Jersey, pp 1–10

Zikopoulos P, Eaton C (2011) Understanding Big Data: analytics for enterprise class hadoop and streaming data. McGraw-Hill Osborne Media, IBM

Kezunovic M, Xie L, Grijalva S (2013) The role of Big Data in improving power system operation and protection. In: IREP symposium—bulk power system dynamics and control—IX (IREP), Rethymnon, Greece

Bosse E, Solaiman B (2016) Information fusion and analytics for Big Data and IoT. Artech House

Li K-C, Jiang H, Yang LT, Cuzzocrea A (2015) Big Data: algorithms, analytics, and applications. CRC Press

Novaes G, Alencar E, Kraj A. Remote conditioning monitoring system for a hybrid wind diesel system-application at Fernando de Naronha Island. http://www.ontario-sea.org. Accessed April 2012

Pinar JM, García FP, Tobias A, Papaelias M (2013) Wind turbine reliability analysis. Renew Sustain Energy Rev 23:463–472CrossRef

10.

García FP, Pinar JM, Papaelias M, Ruiz de la Hermosa R (2012) Wind turbines maintenance management based on FTA and BDD. Renew Energy Power Qual J (10). ISSN 2172-038X (Online)

11.

García FP, Pedregal DJ, Roberts C (2010) Time series methods applied to failure prediction and detection. Reliab Eng Syst Saf 95(6):698–703CrossRef

12.

Ozbek M, Meng F, Rixen DJ (2013) Challenges in testing and monitoring the inoperation vibration characteristics of wind turbines. Mech Syst Signal Process 41(1–2):649–666CrossRef

13.

Molina C (2014) Effects of operating conditions on the acoustic emissions (AE) from planetary gearboxes. Appl Acoust 77:150–158

14.

Ruiz de la Hermosa R, García FP, Dimlaye V, Ruiz-Hernández D (2014) Pattern recognition by wavelet transforms using macro fibre composites transducers. Mech Syst Signal Process 48(1–2):339–350

15.

Nie M, Wang L (2013) Review of condition monitoring and fault diagnosis technologies for wind turbine gearbox. Procedia CIRP 11:287–290CrossRef

16.

Zeng Z, Tao N, Feng L, Li Y, Ma Y, Zhang C (2014) Breakpoint detection of heating wire in wind blade moulds using infrared thermography. Infrared Phys Technol 64:73–78CrossRef

17.

Mylaraswamy D, Olson L, Nwadiogbu E (2007) Engine performance trending. In: AIAC12-HUMS Conference

18.

Yang W, Court R, Jiang J (2013) Wind turbine condition monitoring by the approach of SCADA data analysis. Renew Energy 53(C):365–376

19.

Wymore ML, Van Dam JE, Ceylan H, Qiao D (2015) A survey of health monitoring systems for wind turbines. Renew Sustain Energy Rev 52:976–990CrossRef

20.

Yang W, Court R, Jiang J (2013) Wind turbine condition monitoring by the approach of SCADA data analysis. Renew Energy 53:365–376CrossRef

21.

Sun P, Li J, Wang C, Lei X (2016) A generalized model for wind turbine anomaly identification based on SCADA data. Appl Energy 168(15):550–567CrossRef

22.

Song Z, Jiang Y, Zhang ZJ (2014) Short-term wind speed forecasting with Markov switching model. Appl Energy 130:103–112CrossRef

23.

Zhang H, Yu YJ, Liu ZY (2014) Study on the maximum entropy principle applied to the annual wind speed probability distribution: a case study for observations of intertidal zone anemometer towers of Rudong in East China Sea. Appl Energy 114:931–938CrossRef

24.

Kusiak A, Verma A (2011) The prediction and diagnosis of wind turbine faults. IEEE Trans Sustain Energy 2:87–96

25.

Kim K, Parthasarathy G, Uluyol O, Foslien W, Sheng S, Fleming P (2011) Use of SCADA data for failure detection in wind turbines. In: ASME 2011 5th international conference on energy sustainability. American Society of Mechanical Engineers, pp 2071–2079

26.

Yang W, Tavner PJ, Crabtree CJ, Wilkinson M (2010) Cost-effective condition monitoring for wind turbines. IEEE Trans Indus Electron 57(1):263–271CrossRef

27.

Lebold M, McClintic K, Campbell R, Byington C, Maynard K (2000) Review of vibration analysis methods for gearbox diagnostics and prognostics. In: Proceedings of the 54th meeting of the society for machinery failure prevention technology, vol 634, p 16

28.

Sadegh H, Mehdi AN, Mehdi A (2016) Classification of acoustic emission signals generated from journal bearing at different lubrication conditions based on wavelet analysis in combination with artificial neural network and genetic algorithm. Tribol Int 95:426–434

29.

Sheng S (2012) Wind turbine gearbox condition monitoring round robin study—vibration analysis. Contract 303:275–300

30.

Večeř P, Kreidl M, Šmíd R (2005) Condition indicators for gearbox condition monitoring systems. Acta Polytechnica 45(6)

31.

Wentao S, Changhou L, Dan Z (2008) Bearing fault diagnosis based on feature weighted FCM cluster analysis. In: 2008 international conference on computer science and software engineering, vol 5. IEEE

32.

Zhu J, Nostrand T, Spiegel C, Morton B (2014) Survey of condition indicators for condition monitoring systems. In: Annual conference of the prognostics and health management society, vol 5, pp 1–13

33.

García Márquez FP, Chacón JM (2012) A pattern recognition and data analysis method for maintenance management. Int J Syst Sci 43(6)1014–1028

34.

Asht S, Dass R (2012) Pattern recognition techniques: a review. Int J Comput Sci Telecommun 3(8)

35.

Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press

36.

Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117CrossRef

37.

Masters T (1993) Practical neural network recipes in C++. Academic Press. ISBN 978-0124790407

Titel: Big Data and Wind Turbines Maintenance Management
verfasst von: Alberto Pliego
Raúl Ruiz de la Hermosa
Fausto Pedro García Márquez
Verlag: Springer International Publishing
Buch: Renewable Energies
Print ISBN: 978-3-319-45362-0

Electronic ISBN: 978-3-319-45364-4

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-45364-4_8

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"