Skip to main content

Advertisement

Springer Nature Link
Log in

Wind power generation fault diagnosis based on deep learning model in internet of things (IoT) with clusters

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

With the rapid development of wind power capacity and sustained growth in total operation time, the maintenance of wind turbines is becoming increasingly prominent, so we urgently need to develop effective wind turbine fault diagnosis and prediction system. The main fault characteristics of wind turbines are summarized from two aspects of fault diagnosis and fault prediction. Aiming at the difficult problems of fault diagnosis, we analyze and summarize the research status of fault diagnosis methods based on vibration, electrical signal analysis and pattern recognition algorithm. At the same time, we point out the technical characteristics, limitations and future trends of various methods. Based on the characteristics of mechanical structure and electronic system degradation in wind turbines, we summarize the current research progress and propose a fault prediction method based on physical failure model and data driven model fusion. In this paper, we use the deep learning model in the framework of the internet of things to predict and diagnose the faults of wind power generation. The experimental results show that the algorithm proposed in this paper can predict the fault types and make reasonable diagnosis.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Yin, M., Wang, C., et al.: Comparison and analysis of wind power development. Trans. China Electrotech. Soc. 25(9), 157–162 (2010)

    Google Scholar 

  2. Nie, Q.-W., Gao, W.: A power network fault diagnosis method based on data mining association rules. Power Syst. Prot. Control 37(9), 8–14 (2009)

    Google Scholar 

  3. Li, J., Li, G.-Q.: A survey on application of fault tolerant control in power system. Power Syst. Prot. Control 38(3), 140–146 (2010)

    Google Scholar 

  4. Yang, Z.-J., Wu, H.-B., Ding, M., et al.: Control strategy of doubly-fed wind generation system for power grid fault. Power Syst. Prot. Control 38(1), 14–18 (2010)

    Google Scholar 

  5. Hang, J., Zhang, J.-Z., Cheng, M., et al.: An overview of condition monitoring and fault diagnostic for wind energy conversion system. Trans. China Electrotech. Soc. 28(4), 261–271 (2013)

    Google Scholar 

  6. Han, A.-Y., Zhang, Z., Yin, X.-G., et al.: Research on fault characteristic and grid connecting-point protection scheme for wind power generation with doubly-fed induction generator. Trans. China Electrotech. Soc. 27(4), 233–239 (2012)

    Google Scholar 

  7. Dong, Y., Li, Y., Cao, H., et al.: Real-time health condition evaluation on wind turbines based on operational condition recognition. Proc CSEE 33(11), 88–95 (2013)

    Google Scholar 

  8. Wang, H., Wang, J.: An effective image representation method using kernel classification. In: 2014 IEEE 26th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 853–858. IEEE (2014)

  9. Energy Research Institute.: China wind energy development roadmap 2050. National Development and Reform Commission of P.R. China, China (2011)

  10. Khan, U., Ahmed, N., Mohyud-Din, S.T.: Heat transfer effects on carbon nanotubes suspended nanofluid flow in a channel with non-parallel walls under the effect of velocity slip boundary condition: a numerical study. Neural Comput. Appl. 28(1), 37–46 (2017)

    Article  Google Scholar 

  11. Zhang, S., Wang, H., Huang, W.: Two-stage plant species recognition by local mean clustering and Weighted sparse representation classification. Cluster Comput. 20, 1517–1525 (2017)

    Article  Google Scholar 

  12. Caselitz, P., Giebhardt, J.: Rotor condition monitoring for improved operational safety of offshore wind energy converters. J. Solar Energy Eng. 127(2), 53–261 (2005)

    Article  Google Scholar 

  13. Chen, X.F., Li, J.M., Cheng, H., et al.: Research and application of condition monitoring and fault diagnosis technology in wind turbines. J. Mech. Eng. 47(9), 45–52 (2011)

    Article  Google Scholar 

  14. Ding, S., Zhang, N., Zhang, X., Wu, F.: Twin support vector machine: theory, algorithm and applications. Neural Comput. Appl. 28(11), 3119–3130 (2017)

    Article  Google Scholar 

  15. Hayat, T., Khan, M.I., Waqas, M., Alsaedi, A.: Magnetohydrodynamic stagnation point flow of third-grade liquid toward variable sheet thickness. Neural Comput. Appl. (2017). https://doi.org/10.1007/s00521-016-2827-1

    Article  Google Scholar 

  16. Liu, S., Fu, W., He, L., Zhou, J., Ma, M.: Distribution of primary additional errors in fractal encoding method. Multimed. Tools Appl. 76(4), 5787–5802 (2017)

    Article  Google Scholar 

  17. Huang, W., Wang, H., Zhang, Y., Zhang, S.: A novel cluster computing technique based on signal clustering and analytic hierarchy model using hadoop. Cluster Comput (2017). https://doi.org/10.1007/s10586-017-1205-9

    Article  Google Scholar 

  18. Wang, J., Li, T., Shi, Y.Q., Lian, S., Ye, J.: Forensics feature analysis in quaternion wavelet domain for distinguishing photographic images and computer graphics. Multimed. Tools Appl. 76(22), 23721–23737 (2017)

    Article  Google Scholar 

  19. Bok, K., Hwang, J., Lim, J., Kim, Y., Yoo, J.: An efficient MapReduce scheduling scheme for processing large multimedia data. Multimed. Tools Appl. 76(16), 17273–17296 (2017)

    Article  Google Scholar 

  20. Chen, Q., Zhang, G., Yang, X., Li, S., Li, Y., Wang, H.H.: Single image shadow detection and removal based on feature fusion and multiple dictionary learning. Multimed. Tools Appl. (2017). https://doi.org/10.1007/s11042-017-5299-0

    Article  Google Scholar 

  21. Guo, J.: Smartphone-powered electrochemical biosensing dongle for emerging medical IoTs application. IEEE Trans. Ind. Inform. (2017). https://doi.org/10.1109/tii.2017.2777145

    Article  Google Scholar 

  22. Liu, H., Bolic, M., Nayakand, A., et al.: Taxonomy and challenges of the integration of RFID and wireless sensor networks. IEEE Netw. 22(6), 26–35 (2008)

    Article  Google Scholar 

  23. Englund, C., Wallin, H.: RFID in wireless sensor network, EX034/2004. Communication Systems Group, Department of Signals and Systems, Chalmers University of Technology, Sweden (2004)

  24. Ahmad, A., Hanzálek, Z.: An energy efficient schedule for IEEE 802.15. 4/zigbee cluster tree WSN with multiple collision domains and period crossing constraint. IEEE Trans. Ind. Inform. 14(1), 2–23 (2018)

    Article  Google Scholar 

  25. Zhang, S., Wang, H., Huang, W., You, Z.: Plant diseased leaf segmentation and recognition by fusion of superpixel, K-means and PHOG. Optik-Int. J. Light Electron Optics 157, 866–872 (2018)

    Article  Google Scholar 

  26. Chen, M., Xiao, X., Guerrero, J.M.: Secondary restoration control of islanded microgrids with decentralized event-triggered strategy. Ind. Inform, IEEE Trans. (2017). https://doi.org/10.1109/TII.2017.2784561

    Book  Google Scholar 

  27. Laserson, J.: From neural networks to deep learning: zeroing in on the human brain. XRDS Crossroads ACM Mag. Stud. 18(1), 29–34 (2011)

    Article  MathSciNet  Google Scholar 

  28. Cha, Y.J., Choi, W., Büyükztürk, O.: Deep learning based crack damage detection using convolutional neural networks. Comput. Aided Civ. Infrastruct. Eng. 32(5), 1–378 (2017)

    Article  Google Scholar 

  29. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning, pp. 2104–2116. Addison-Wesley, Reading (1989)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Control and Computer Engineering, North China Electric Power University, Beijing, China

    Fei Chen

  2. School of Energy & Power and Mechanical Engineering, North China Electric Power University, Beijing, China

    Zhongguang Fu & Zhiling Yang

Authors
  1. Fei Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhongguang Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhiling Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fei Chen.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, F., Fu, Z. & Yang, Z. Wind power generation fault diagnosis based on deep learning model in internet of things (IoT) with clusters. Cluster Comput 22 (Suppl 6), 14013–14025 (2019). https://doi.org/10.1007/s10586-018-2171-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-018-2171-6

Keywords