Skip to main content
SpringerLink
Log in

Fault Identification in Doubly fed Induction Generator using FFT and Neural Networks

  • Published:
Journal of Control, Automation and Electrical Systems Aims and scope Submit manuscript

Abstract

This paper presents a fault identification system for doubly fed induction generator. The proposed system is designed to identify single-phase faults and load switching events on an isolated load. Firstly, the system preprocess the stator line current data by the fast Fourier transform (FFT). In order to reduce the dimensionality of the FFT output data, the principal component analysis method is used. The fault identification stage is based on artificial neural network (ANN). Also, a post-processing is employed in order to increase the network reliability, which reduces the error of ANN. The proposed system is simulated and experimentally validated on different voltage, speed and load conditions.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

References

  • Alaya, J. B., Khedher, A., & Mimouni, M. F. (2011). Nonlinear vector control strategy applied to a variable speed DFIG generation system. In Eighth International Multi-Conference on Systems, Signals and Devices, pp 1–8.

  • Attoui, I., & Omeiri, A. (2014). Contribution to the fault diagnosis of a doubly fed induction generator for a closed-loop controlled wind turbine system associated with a two-level energy storage system. Electric Power Components and Systems, 42, 1727–1742.

    Article  Google Scholar 

  • Boldea, I. (2006). Variable speed generators. Timisoaria: Taylor & Francis.

    Google Scholar 

  • Decanini, J. G. M. S., Tonelli-Neto, M. S., Malange, F. C. V., & Minussi, C. R. (2011). Detection and classification of voltage disturbances using a Fuzzy-ARTMAP-wavelet network. Electric Power Systems Research, 81, 2057–2065.

    Article  Google Scholar 

  • El-Naggar, A., & Erlich, I. (2016). Analysis of fault current contribution of Doubly-Fed Induction Generator Wind Turbines during unbalanced grid faults. Renewable Energy, 91, 137–146.

    Article  Google Scholar 

  • Frigo, M., & Johnson, S. G. (2005). The design and implementation of FFTW3. Proceedings of the IEEE, 93, 216–231.

    Article  Google Scholar 

  • Granados-Lieberman, D., Romero-Troncoso, R. J., Osornio-Rios, R. A., Garcia-Perez, A., & Cabal-Yepez, E. (2011). Techniques and methodologies for power quality analysis and disturbances classification in power systems: A review. IET Generation, Transmission & Distribution, 5, 519.

    Article  Google Scholar 

  • Haykin, S., & Wichern, D. W. (1999). Redes neurais - princípios e prática (neural networks - principles and practice). Porto Alegre: Bookman.

  • Huang, N., Xu, D., Liu, X., & Lin, L. (2012). Power quality disturbances classification based on S-transform and probabilistic neural network. Neurocomputing, 98, 12–23.

    Article  Google Scholar 

  • Johnson, R. A., & Wichern, D. W. (2007). Applied multivariate statistical analysis. New Jersey, EUA: Prentice Hall.

  • Justo, J. J., Mwasilu, F., & Jung, J. (2015). Doubly-fed induction generator based wind turbines: A comprehensive review of fault ride-through strategies. Renewable and Sustainable Energy Reviews, 45, 447–467.

    Article  Google Scholar 

  • Marquardt, D. (1963). An algorithm for least squares estimation of non-linear parameters. Journal of the Society for Industrial and Applied Mathematics, 11, 431–441.

    Article  MATH  Google Scholar 

  • Masoum, M. A. S., Jamali, S., & Ghaffarzadeh, N. (2010). Detection and classification of power quality disturbances using discrete wavelet transform and wavelet networks. IET Science, Measurement & Technology, 4, 193–205.

    Article  Google Scholar 

  • Rodríguez, A., Aguado, J. A., Martín, F., López, J. J., Munhõz, F., & Ruiz, J. E. (2012). Rule-based classification of power quality disturbances using S-transform. Electric Power Systems Research, 86, 113–121.

    Article  Google Scholar 

  • Silva, I. N., Spatti, D. H., & Flauzino, R. A. (2010). Redes Neurais artificiais para engenharia e ciencias aplicadas - curso prático (Artificial Neural Networks for engineering and applied science - practical course.). São Paulo, SP: Artliber editora Ltda.

  • Smith, L.I. (2002). A tutorial on principal components analysis. http://www.ce.yildiz.edu.tr/personal/songul/file/1097/principal_components.

  • Smith, S. (1999). The scientist and engineer’s guide to digital signal processing. San Diego, CA: California Technical Publishing.

  • Stojcic, G., Pasanbegovic, K., & Wolbank, T.M. (2013). Detecting faults in Doubly fed induction generator by rotor side transient current measurement. IEEE Energy Conversion Congress and Exposition, 50, 4420–4425.

  • Uyar, Mu, Yildirim, S., & Gencoglu, Muhsin Tunay. (2009). An expert system based on S-transform and neural network for automatic classification of power quality disturbances. Expert Systems with Applications, 36, 5962–5975.

  • Ventosa, Se, Simon, C., Schimmel, M., Danobeitia, J. J., & Manuel, A. (2008). S-transform, time-frequency analysis, wavelet transform. IEEE Transactions on Signal Processing, 56, 2771–2780.

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, University of São Paulo - USP/EESC/SEL, Av. Trabalhador São-carlense, 400, Arnold Schimidt, São Carlos, SP, CEP 13566-590, Brazil

    Marcelo Patrício de Santana, José Roberto Boffino de Almeida Monteiro, Fabbio Anderson Silva Borges, Geyverson Teixeira de Paula, Thales Eugênio Portes de Almeida, William César de Andrade Pereira & Allan Gregori de Castro

Authors
  1. Marcelo Patrício de Santana
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. José Roberto Boffino de Almeida Monteiro
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fabbio Anderson Silva Borges
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Geyverson Teixeira de Paula
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Thales Eugênio Portes de Almeida
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. William César de Andrade Pereira
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Allan Gregori de Castro
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marcelo Patrício de Santana.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Patrício de Santana, M., Boffino de Almeida Monteiro, J.R., Silva Borges, F.A. et al. Fault Identification in Doubly fed Induction Generator using FFT and Neural Networks. J Control Autom Electr Syst 28, 228–237 (2017). https://doi.org/10.1007/s40313-016-0298-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40313-016-0298-3

Keywords

Search

Navigation