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Erschienen in:
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2017 | OriginalPaper | Buchkapitel

A Diagnostic Knowledge Model of Wind Turbine Fault

verfasst von : Hongwei Wang, Wei Liu, Zhanli Liu

Erschienen in: Intelligent Robotics and Applications

Verlag: Springer International Publishing

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Abstract

With the development of the wind power industry, wind power has become one of the main green generation energy. At the same time, with the wind power installed capacity increasing, the failure rate gradually growth. As wind turbine is a complex electromechanical equipment, the fault diagnosis for this kind of equipment is also a complicated process. Focused on the current shortage of fault diagnosis knowledge representation, this paper proposes a diagnostic knowledge model for wind turbine and also elaborates the model structure definition with a target to ensure the accuracy of fault diagnosis. Besides, this model can also offer assistance reference model for researchers in related fields to develop advanced methods for sharing and reuse of diagnostic knowledge.

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Vorheriges Kapitel A Robot Architecture of Hierarchical Finite State Machine for Autonomous Mobile Manipulator
Nächstes Kapitel Robust Object Tracking via Structure Learning and Patch Refinement in Handling Occlusion
Literatur
1.
Global Wind Energy Council: Wind in power 2016 European statistics, pp. 6–7 (2016)
2.
Hatch, C.: Improved wind turbine condition monitoring using acceleration enveloping. Orbit 61, 58–61 (2004)
3.
Kusiak, A., Li, W.: The prediction and diagnosis of wind turbine faults. Renew. Energy 36, 16–23 (2011)CrossRef
4.
García Márquez, F., Tobias, A., Pinar Pérez, J., Papaelias, M.: Condition monitoring of wind turbines: techniques and methods. Renew. Energy 46, 169–178 (2012)CrossRef
5.
Milborrow, D.: Operation and maintenance costs compared and revealed. Windstats Newslett. 19, 1–3 (2006)
6.
Lau, B.C.P., Ma, E.W.M., Pecht, M.: Review of offshore wind turbine failures and fault prognostic methods. In: 2012 IEEE Conference on Prognostics and System Health Management (PHM), pp. 1–5. IEEE (2012)
7.
Takoutsing, P., Wamkeue, R., Ouhrouche, M., Slaoui-Hasnaoui, F., Tameghe, T., Ekemb, G.: Wind turbine condition monitoring: state-of-the-art review, new trends, and future challenges. Energies 7, 2595–2630 (2014)CrossRef
8.
Birolini, A.: Reliability Engineering, vol. 5. Springer, Heidelberg (2007)MATH
9.
10.
Nie, M., Wang, L.: Review of condition monitoring and fault diagnosis technologies for wind turbine gearbox. Procedia CIRP 11, 287–290 (2013)CrossRef
11.
Liu, W., Tang, B., Han, J., Lu, X., Hu, N., He, Z.: The structure healthy condition monitoring and fault diagnosis methods in wind turbines: a review. Renew. Sustain. Energy Rev. 44, 466–472 (2015)CrossRef
12.
Amirat, Y., Benbouzid, M., Al-Ahmar, E., Bensaker, B., Turri, S.: A brief status on condition monitoring and fault diagnosis in wind energy conversion systems. Renew. Sustain. Energy Rev. 13, 2629–2636 (2009)CrossRef
13.
Kithil, R.: Case study of Lightning Damage to wind turbine blade. National Lighting Safety Institute (NLSI) (2008)
14.
Yang, M., Chengbing, H., Xinxin, F.: Institutions function and failure statistic and analysis of wind turbine. Phys. Procedia 24, 25–30 (2012)CrossRef
15.
16.
Purarjomandlangrudi, A., Nourbakhsh, G., Esmalifalak, M., Tan, A.: Fault detection in wind turbine: a systematic literature review. Wind Eng. 37, 535–548 (2013)CrossRef
17.
Babu, J.R., Jithesh, S.V.: Breakdown risks in wind energy turbines. Pravartak J. Insur. Risk Manag. National Insur. Acad. Pun 3(3) (2008)
18.
The Confederation of Fire Protection Association CFP A Europe.: Wind turbines fire protection guideline (2010)
19.
Benbouzid, M.: Bibliography on induction motors faults detection and diagnosis. IEEE Trans. Energy Convers. 14, 1065–1074 (1999)CrossRef
20.
Ghaedi, A., Abbaspour, A., Fotuhi-Firuzabad, M., Moeini-Aghtaie, M.: Toward a comprehensive model of large-scale dfig-based wind farms in adequacy assessment of power systems. IEEE Trans. Sustain. Energy 5, 55–63 (2014)CrossRef
21.
Stubkier, S., Dyrbye, E.: AVN energy designs a new hydraulic yaw system and reduces the fatigue loads of the wind turbine. Mod. Energy Rev. 3(2), 45 (2011)
22.
Chen, R., Zhou, Z., Liu, Q., Pham, D.T., Zhao, Y., Yan, J., Wei, Q.: Knowledge modeling of fault diagnosis for rotating machinery based on ontology. In: 2015 International Conference on Industrial Informatics (INDIN), pp. 1050–1055. IEEE (2015)
23.
Jeffries, W.Q., Chambers, J.A., Infield, D.G.: Experience with bicoherence of electrical power for condition monitoring of wind turbine blades. IEE Proc.-Vis. Image Sig. Process. 145(3), 141–148 (1998)CrossRef
24.
Jasiūnienė, E., Raišutis, R., Šliteris, R., Voleišis, A., Jakas, M.: Ultrasonic NDT of wind turbine blades using contact pulse-echo immersion testing with moving water container. Ultragarsas “Ultrasound” 63(3), 28–32 (2016)
25.
Liu, W., Tang, B., Jiang, Y.: Status and problems of wind turbine structural health monitoring techniques in China. Renew. Energy 35, 1414–1418 (2010)CrossRef
26.
Benbouzid, M.E.H., Kliman, G.B.: What stator current processing-based technique to use for induction motor rotor faults diagnosis? IEEE Trans. Energy Convers. 18(2), 238–244 (2003)CrossRef
27.
Benbouzid, M.E.H.: A review of induction motors signature analysis as a medium for faults detection. IEEE Trans. Industr. Electron. 47(5), 984–993 (2000)CrossRef
28.
Wilkinson, M.R., Spinato, F., Tavner, P.J.: Condition monitoring of generators & other subassemblies in wind turbine drive trains. In: IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, pp. 388–392. IEEE (2007)
29.
Yang, W., Tavner, P.J., Crabtree, C.J., Wilkinson, M.: Research on a simple, cheap but globally effective condition monitoring technique for wind turbines. In: 2008 18th International Conference on Electrical Machines, ICEM 2008, pp. 1–5. IEEE (2008)
30.
Kim, K.: Parlos, A.: Induction motor fault diagnosis based on neuropredictors and wavelet signal processing. IEEE/ASME Trans. Mechatron. 7, 201–219 (2002)CrossRef
31.
Ahadi, A.: Wind turbine fault diagnosis techniques and related algorithms. Int. J. Renew. Energy Res. (IJRER) 6(1), 80–89 (2016)
32.
Zhang, Z., Verma, A., Kusiak, A.: Fault analysis and condition monitoring of the wind turbine gearbox. IEEE Trans. Energy Convers. 27, 526–535 (2012)CrossRef
Metadaten
Titel
A Diagnostic Knowledge Model of Wind Turbine Fault
verfasst von
Hongwei Wang
Wei Liu
Zhanli Liu
Copyright-Jahr
2017
Buch
Intelligent Robotics and Applications

Print ISBN: 978-3-319-65297-9

Electronic ISBN: 978-3-319-65298-6

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-65298-6_40