Abstract
To increase the performance and reliability of next-generation wind turbines, the technology must continue to evolve building on earlier successes in wind energy and other fields. This chapter provides an introduction and in-depth survey of four emerging technologies: permanent magnetic direct drive, 3D printing, anti-icing and deicing, and data-mining techniques, particularly used for wind energy. The merits of each technology are briefly described as follows. The wind turbines with permanent magnetic direct-drive generators could offer higher efficiency of energy conversion and lower maintenance cost than traditional wind turbine designs with gearboxes. The 3D printing technology opens a new window for rapid design and manufacturing of wind turbine systems, e.g., use of 3D printing of wind turbine blade molds for new blade design. The anti-icing and deicing technology could improve the performance and reliability of wind turbines and lower the safety risks for wind turbines installed in cold-climate areas. Various data-mining techniques take full advantage of the huge amounts of available data from wind turbines and/or wind farms, acquire useful information within, and eventually lower the wind energy cost. The fundamental concepts, main classifications, and key applications and contributions of these four types of emerging technologies are elaborated.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
An X, Jiang D, Liu C, Zhao M (2011) Wind farm power prediction based on wavelet decomposition and chaotic time series. Expert Syst Appl 38(9):11280–11285
Anagnostopoulos JS, Koukouvinis PK, Stamatelos FG (2012) Papantonis DE optimal design and experimental validation of a Turgo model hydro turbine. In: ASME 2012 11th biennial conference on engineering systems design and analysis, ASME paper no. ESDA2012–82565, Nantes, France, 2–4 July 2012
Antoni J, Randall R (2006) The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines. Mech Syst Signal Process 20(2):308–331
Bang D-j, Polinder H, Shrestha G, Abraham Ferreira J (2008a) Promising direct-drive generator system for large wind turbines. EPE J 18(3):7–13
Bang D, Polinder H, Shrestha G, Ferreira JA (2008b) Review of generator systems for direct-drive wind turbines. In: European wind energy conference & exhibition, Belgium
Barbounis TG, Theocharis JB, Alexiadis MC, Dokopoulos PS (2006) Long-term wind speed and power forecasting using local recurrent neural network models. IEEE Trans Energy Convers 21(1):273–284
Bassett K, Carriveau R, Ting D-K (2015) 3D printed wind turbines part 1: design considerations and rapid manufacture potential. Sustainable Energy Technol Assess 11:186–193
Bathurst G, Strbac G (2003) Value of combining energy storage and wind in short-term energy and balancing markets. Electr Power Syst Res 67(1):1–8
Blonbou R (2011) Very short-term wind power forecasting with neural networks and adaptive Bayesian learning. Renew Energy 36(3):1118–1124
Blonbou R, Monjoly S, Dorville J-F (2011) An adaptive short-term prediction scheme for wind energy storage management. Energy Convers Manag 52(6):2412–2416
Boluk Y (1996) Adhesion of freezing precipitates to aircraft surfaces, Optima Speciality Chemicals & Technology Inc., Montreal, Quebec
Botta G, Cavaliere M, Holttinen H (1998) Ice accretion at acqua spruzza and its effects on wind turbine operation and loss of energy production. BOREAS IV FMI, Hetta, pp 77–86
Carpinone A, Langella R, Testa A, Giorgio M (2010) Very short-term probabilistic wind power forecasting based on Markov chain models. In: 2010 IEEE 11th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), Singapore. IEEE, pp 107–112, 14–17 June 2010
Caselitz P, Giebhardt J, Mevenkamp M, Reichardt M (1997) Application of condition monitoring systems in wind energy converters. In: EWEC-conference. Bookshop for Scientific Publications, Dublin, Ireland, pp 579–582, October, 1997
Catalão JPdS, Pousinho HMI, Mendes VMF (2009) An artificial neural network approach for short-term wind power forecasting in Portugal. In: Intelligent system applications to power systems, 2009. ISAP’09. 15th international conference on, 2009. IEEE, pp 1–5
Catalão JPS, Pousinho HMI, Mendes VMF (2011) Short-term wind power forecasting in Portugal by neural networks and wavelet transform. Renew Energy 36(4):1245–1251
Cheng M, Hua W, Zhang J, Zhao W (2011) Overview of stator-permanent magnet brushless machines. IEEE Trans Ind Electron 58(11):5087–5101
Cheng M, Zhu Y (2014) The state of the art of wind energy conversion systems and technologies: a review. Energy Convers Manag 88:332–347
Colak I, Sagiroglu S, Yesilbudak M (2012) Data mining and wind power prediction: a literature review. Renew Energy 46:241–247
Dalili N, Edrisy A, Carriveau R (2009) A review of surface engineering issues critical to wind turbine performance. Renew Sust Energ Rev 13(2):428–438
De Giorgi MG, Ficarella A, Tarantino M (2011) Error analysis of short term wind power prediction models. Appl Energy 88(4):1298–1311
Faulstich S, Hahn B, Tavner PJ (2011) Wind turbine downtime and its importance for offshore deployment. Wind Energy 14(3):327–337
Fikke SM, Ronsten G, Heimo A, Kunz S, Ostrozlik M, Persson P, Sabata J,Wareing B,Wichura B, Chum J (2006) COST 727: atmospheric icing on structures: measurements and data collection on icing: state of the art. Meteo Schweiz, Zurich, Switzerland
Foley AM, Leahy PG, Marvuglia A, McKeogh EJ (2012) Current methods and advances in forecasting of wind power generation. Renew Energy 37(1):1–8
Fortin G, Perron J, Ilinca A (2005) Behaviour and modeling of cup anemometers under Icing conditions. IWAIS XI, Montréal, p 6
Friedrich K, Lukas M (2017) State-of-the-art and new technologies of direct drive wind turbines. In: Uyar TS (ed) Towards 100% renewable energy techniques, costs and regional case-studies. Springer International Publishing, Cham, pp 33–50
Goldwind (2017) 1.5 MW PMDD wind turbine. http://www.goldwindamericas.com/sites/default/files/Goldwind-Brochure-1.5-Web.pdf. Accessed 28 Aug 2017
Gong X (2012) Online nonintrusive condition monitoring and fault detection for wind turbines. The University of Nebraska-Lincoln, Lincoln
Google Ngram Viewer 2017 (2017) Additive manufacturing. Google. https://books.google.com/ngrams/. Accessed 12 Nov 2017
Grady S, Hussaini M, Abdullah MM (2005) Placement of wind turbines using genetic algorithms. Renew Energy 30(2):259–270
Hatch C (2004) Improved wind turbine condition monitoring using acceleration enveloping. Orbit 61:58–61
Hatch C, Weiss A, Kalb M (2010) Cracked bearing race detection in wind turbine gearboxes. Orbit 30(1):40–47
Hong Y-Y, Chang H-L, Chiu C-S (2010) Hour-ahead wind power and speed forecasting using simultaneous perturbation stochastic approximation (SPSA) algorithm and neural network with fuzzy inputs. Energy 35(9):3870–3876
Howey DA, Bansal A, Holmes AS (2011) Design and performance of a centimetre-scale shrouded wind turbine for energy harvesting. Smart Mater Struct 20(8):085021
ISO-12494 (2001) Atmospheric icing of structures. ISO Copyright Office, Geneva
Johnson PL, Negnevitsky M, Muttaqi KM (2007) Short term wind power forecasting using adaptive neuro-fuzzy inference systems. In: Power engineering conference, 2007. AUPEC 2007. Australasian Universities. Perth, Australia. IEEE pp 1–6, 9–12 Dec 2007
Jursa R, Rohrig K (2008) Short-term wind power forecasting using evolutionary algorithms for the automated specification of artificial intelligence models. Int J Forecast 24(4):694–709
Katsigiannis Y, Tsikalakis A, Georgilakis P, Hatziargyriou N (2006) Improved wind power forecasting using a combined neuro-fuzzy and artificial neural network model. Advances in Artificial Intelligence, Proceedings of 4th Helenic Conference on AI, SETN 2006, Heraklion, Crete, Greece, pp. 105–115, 18–21 May 2006
Kusiak A, Li W (2011) The prediction and diagnosis of wind turbine faults. Renew Energy 36(1):16–23
Kusiak A, Zhang Z (2010) Short-horizon prediction of wind power: a data-driven approach. IEEE Trans Energy Convers 25(4):1112–1122
Kusiak A, Zheng H, Song Z (2009a) Models for monitoring wind farm power. Renew Energy 34(3):583–590
Kusiak A, Zheng H, Song Z (2009b) Short-term prediction of wind farm power: a data mining approach. IEEE Trans Energy Convers 24(1):125–136
Kusiak A, Zheng H, Song Z (2009c) Wind farm power prediction: a data-mining approach. Wind Energy 12(3):275–293
Laakso T, Holttinen H, Ronsten G, Tallhaug L, Horbaty R, Baring-Gould I, Lacroix A, Peltola E, Tammelin B (2003) State-of-the-art of wind energy in cold climates. IEA Annex XIX 24:53
Laakso T, Talhaug L, Ronsten G, Horbaty R, Baring-Gould I, Lacroix A, Peltola E (2005) Wind energy projects in cold climates. Int Energy Agency 36:21–24
Li S, Wunsch DC, O’Hair E, Giesselmann MG (2001) Comparative analysis of regression and artificial neural network models for wind turbine power curve estimation. J Sol Energy Eng 123(4):327–332
Liao TW, Triantaphyllou E (2008) Recent advances in data mining of enterprise data: algorithms and applications, vol 6. World Scientific, Singapore
Liu H, Tian H-Q, Chen C, Li Y-f (2010) A hybrid statistical method to predict wind speed and wind power. Renew Energy 35(8):1857–1861
Mueller M, Zavvos A (2013) Electrical generators for direct drive systems: a technology over. In: Mueller M, Polinder H (eds) Electrical drives for direct drive renewable energy systems. Woodhead Publishing, Oxford
Muñoz CQG, Márquez FPG, Tomás JMS (2016) Ice detection using thermal infrared radiometry on wind turbine blades. Measurement 93:157–163
Negnevitsky M, Johnson P (2008) Very short term wind power prediction: a data mining approach. In: Power and energy society general meeting-conversion and delivery of electrical energy in the 21st century, 2008 IEEE, 2008. IEEE, pp 1–3
Negnevitsky M, Mandal P, Srivastava AK (2009) Machine learning applications for load, price and wind power prediction in power systems. In: 2009 15th International Conference on Intelligent System Applications to Power Systems (ISAP), Curitiba, Brazil. IEEE pp 1–6, 8–12 Nov 2009
Parent O, Ilinca A (2011) Anti-icing and de-icing techniques for wind turbines: critical review. Cold Reg Sci Technol 65(1):88–96
Park J, Law KH (2016) A data-driven, cooperative wind farm control to maximize the total power production. Appl Energy 165:151–165
Pinson P, Madsen H (2008) Probabilistic forecasting of wind power at the minute time-scale with markov-switching autoregressive models. In: Proceedings of the 10th International Conference on Probablistic Methods Applied to Power Systems, 2008, Rincon, PR, USA . IEEE, pp 1–8, 25–29 May 2008
Popa LM, Jensen B-B, Ritchie E, Boldea I (2003) Condition monitoring of wind generators. In: 38th IAS Annual Meeting on Conference Record of the Industry Applications Conference, 2003, Salt Lake City, UT, USA. IEEE pp 1839–1846, 12–16 October 2003
Richert F (1996) Is rotorcraft icing knowledge transferable to wind turbines. BOREAS III FMI, Saariselkä, pp 366–380
Semken RS, Polikarpova M, Röyttä P, Alexandrova J, Pyrhönen J, Nerg J, Mikkola A, Backman J (2012) Direct-drive permanent magnet generators for high-power wind turbines: benefits and limiting factors. IET Renewable Power Generation 6(1):1–8
Senjyu T, Yona A, Urasaki N, Funabashi T (2006) Application of recurrent neural network to long-term-ahead generating power forecasting for wind power generator. In: Power systems conference and exposition, 2006. PSCE’06. 2006 IEEE PES, 2006, Atlanta, GA, USA. IEEE pp 1260–1265, 29 Oct–1 Nov 2006
Shi J, Yang Y, Wang P, Liu Y, Han S (2010) Genetic algorithm-piecewise support vector machine model for short term wind power prediction. In: Intelligent control and automation (WCICA), 2010 8th world congress on, 2010. IEEE, pp 2254–2258
Shun S, Ahmed NA (2012) Rapid prototyping of aerodynamics research models. In: Applied mechanics and materials. Trans Tech Publications, Switzerland, pp 2016–2025
Swainson WK (1977) Method, medium and apparatus for producing three-dimensional figure product. Google Patents
Tammelin B, Holttinen H, Morgan C, Richert F, Seifert H, Säntti K, Vølund P (2000) Wind energy production in cold climate. Finnish Meteorological Institute, Helsinki
Tammelin B, Säntti K, Dobech H, Durstewich M, Ganander H, Kury G, Laakso T, Peltola E, Ronsten R (2005) Wind turbines in icing environment: improvement of tools for siting, certification and operation-NEW ICETOOLS. Finnish Meteorological Institute, Finland
Tan PN, Steinbach M, Kumar V (2006) Introduction to data mining. Addison-Wesley, Boston
Tchakoua P, Wamkeue R, Ouhrouche M, Slaoui-Hasnaoui F, Tameghe TA, Ekemb G (2014) Wind turbine condition monitoring: state-of-the-art review, new trends, and future challenges. Energies 7(4):2595–2630
U.S. Department of Energy (2016) Transforming wind turbine blade mold manufacturing with 3D printing. https://www.youtube.com/watch?time_continue=241&v=tRiULaXzRNo. Accessed 24 Nov 2017
Ultimaker (2017) Vertical axis wind turbine model. https://ultimaker.com/en/resources/19639-vertical-axis-wind-turbine. Accessed 12 Nov 2017
Vargas L, Paredes G, Bustos G (2010) Data mining techniques for very short term prediction of wind power. In: Bulk power system dynamics and control (iREP)-VIII (iREP), 2010 iREP symposium, 2010. IEEE, pp 1–7
Wang L, Dong L, Hao Y, Liao X (2009) Wind power prediction using wavelet transform and chaotic characteristics. In: World non-grid-connected wind power and energy conference, 2009. WNWEC 2009, Nanjing, China, IEEE, pp 1–5, 24–26 Sept 2009
Wilkinson MR, Spinato F, Tavner PJ (2007) Condition monitoring of generators & other subassem- blies in wind turbine drive trains. In: 2007 IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, Cracow, Poland. IEEE pp 388–392, 6–8 Sept 2007
Wu Y-K, Lee C-Y, Tsai S-H, Yu S-N (2010) Actual experience on the short-term wind power fore- casting at Penghu—from an island perspective. In: 2010 International Conference on Power System Technology, Hangzhou, China. IEEE, pp 1–8, 24–28 Oct 2010
Xia J, Zhao P, Dai Y (2010) Neuro-fuzzy networks for short-term wind power forecasting. In: 2010 International Conference on Power System Technology, Hangzhou, China. IEEE pp 1–5, 24–28 Oct 2010
Xin W, Liu Y, Li X (2010) Short-term forecasting of wind turbine power generation based on genetic neural network. In: Intelligent control and automation (WCICA), 2010 8th world congress on, 2010. IEEE, pp 5943–5946
Yang W, Tavner PJ, Crabtree CJ, Feng Y, Qiu Y (2014) Wind turbine condition monitoring: technical and commercial challenges. Wind Energy 17(5):673–693
Yazidi A, Henao H, Capolino G, Artioli M, Filippetti F, Casadei D (2005) Flux signature analysis: an alternative method for the fault diagnosis of induction machines. In: Power tech, 2005 IEEE Russia. IEEE, pp 1–6
Zayas J, Johnson M (2016) Transforming wind turbine blade mold manufacturing with 3D printing. U.S. DOE's Office of Energy Efficiency and Renewable Energy (EERE), USA
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this chapter
Cite this chapter
Hu, W. (2018). Emerging Technologies for Next-Generation Wind Turbines. In: Hu, W. (eds) Advanced Wind Turbine Technology. Springer, Cham. https://doi.org/10.1007/978-3-319-78166-2_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-78166-2_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-78165-5
Online ISBN: 978-3-319-78166-2
eBook Packages: EnergyEnergy (R0)