A New TSA-Fuzzy Logic Based Diagnosis of Rotor Winding Inter-turn Short Circuit Fault in Wind Turbine Based on DFIG Under Different Operating Wind Speeds

  • Hamza SabirEmail author
  • Mohammed Ouassaid
  • Nabil Ngote
Conference paper
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 7)


Doubly-fed induction generators (DFIGs) are the most utilized generator types of wind turbine systems, thanks to their important efficiency, elevated power factor, quicker response and robust construction. However, they may be subject of many kinds of defect. Condition monitoring methods and early failure diagnosis algorithms for wind turbine installation have become a fundamental practice as they serve to enhance wind farm dependability, performance and overall productivity. In this context, an advanced diagnostic technique for the detection of rotor inter-turn short circuit residual faults, based on the combination of the Time Synchronous Averaging (TSA) technique and Fuzzy Logic (FL) is established. In fact, rotor residual failure cannot be detected directly by analyzing the stator current, especially in the low wind speed (WS) case. The RMS of residual current and the value of wind speed will be used as inputs for the fuzzy logic bloc in order to give the decision about the state of the rotor. The proposed strategy has been implemented and verified using simulations built in MatLab \(\circledR \) SIMULINK environment. The simulation results prove the efficiency and the reliability of the proposed approach.


Doubly-fed induction generators Fault detection Time Synchronous Averaging technique Fuzzy logic 


  1. 1.
    Kaidis, C., Uzunoglu, B., Filippos, A.: Wind turbine reliability estimation for different assemblies and failure severity categories. IET Renew. Power Gener. 9, 892–899 (2015)CrossRefGoogle Scholar
  2. 2.
    Jaiswal, S., Pahuja, G.L.: Effect of reliability of wind power converters in productivity of wind turbine. In: IEEE 6th India International Conference on Power Electronics (IICPE), pp. 1–6 (2014)Google Scholar
  3. 3.
    Albarbar, A., Teay, S., Batunlu, C.: Smart sensing system for enhancing the reliability of power electronic devices used in wind turbines. Int. J. Smart Sens. Intell. Syst. 10, 407–424 (2017)Google Scholar
  4. 4.
    Chauhan, U., Pahuja, G.L., Singh, V., Rani, A.: Reliability analysis of wind turbine system using importance measures. In: IEEE India Conference (INDICON), pp. 1–5 (2015)Google Scholar
  5. 5.
    Wenxian, Y., Tavner, P.J., Court, R.: An online technique for condition monitoring the induction generators used in wind and marine turbines. Mech. Syst. Signal Process. 38, 103–112 (2013)CrossRefGoogle Scholar
  6. 6.
    Sellami, T., Berriri, H., Mimouni, M.F.: Impact of inter-turn short-circuit fault on wind turbine driven squirrel-cage induction generator systems. In: Conférence Internationale en Sciences et Technologies Electriques au Maghreb CISTEM Tunis, Tunisia (2014)Google Scholar
  7. 7.
    Tchakoua, P., Wamkeue, R., Ouhrouche, M.: Wind turbine condition monitoring: state-of-the-art review, new trends, and future challenges. Energies 7, 2595–2630 (2014)CrossRefGoogle Scholar
  8. 8.
    Marquez, F., Tobias, A., Perez, J., Papaelias, M.: Condition monitoring of wind turbines: techniques and methods. Renew. Energy 46, 169–178 (2012)CrossRefGoogle Scholar
  9. 9.
    Bechhoefer, E., Kingsley, M.: A review of time synchronous average algorithms. In: Conference of the Prognostics and Health Management Society, San Diego, pp. 24–33 (2009)Google Scholar
  10. 10.
    Tavner, P.J.: Review of condition monitoring of rotating electrical machines. IET Electr. Power Appl. 2(4), 215–247 (2008)CrossRefGoogle Scholar
  11. 11.
    Kandukuri, S.T., Klausen, A., Karimi, H.R., Robbersmyr, K.G.: A review of diagnostics and prognostics of low-speed machinery towards wind turbine farm-level health management. Renew. Sustain. Energy Rev. 53, 697–708 (2016)CrossRefGoogle Scholar
  12. 12.
    Balasubramanian, A., Muthu, R.: Model based fault detection and diagnosis of doubly fed induction generators. Energy Procedia 117, 935–942 (2017)CrossRefGoogle Scholar
  13. 13.
    Ngote, N., Guedira, S., Cherkaoui, M.: Conditioning of a statistical indicator for the detection of an asynchronous machine rotor faults. Mech. Ind. 13(3), 197–203 (2012)CrossRefGoogle Scholar
  14. 14.
    Abdelmaleka, S., Rezazib, S., Azar, A.T.: Sensor faults detection and estimation for a DFIG equipped wind turbine. Energy Procedia 139, 3–9 (2017)CrossRefGoogle Scholar
  15. 15.
    Toma, S., Capocchi, L.: Wound rotor induction generator inter-turn short-circuits diagnosis using a new digital neural network. IEEE Trans. Ind. Electron. 6, 4043–4052 (2013)CrossRefGoogle Scholar
  16. 16.
    Merabet, H., Bahi, T., Halem, N.: Condition monitoring and fault detection in wind turbine based on DFIG by the fuzzy logic. Energy Procedia 74, 518–528 (2015)CrossRefGoogle Scholar
  17. 17.
    Azgomi, H.F., Poshtan, J., Poshtan, M.: Experimental validation on stator fault detection via fuzzy logic. In: 3rd International Conference on Electric Power and Energy Conversion Systems, pp. 1–6 (2013)Google Scholar
  18. 18.
    Aydin, I., Karakose, M., Akin, E.: A new real-time fuzzy logic based diagnosis of stator faults for inverter-fed induction motor under low speeds. In: 2016 IEEE 14th International Conference on Industrial Informatics (INDIN), pp. 446–451 (2016)Google Scholar
  19. 19.
    Noureddine, L., Hafaifa, A., Kouzou, A.: Fuzzy logic system for BRB defect diagnosis of SCIG-based wind energy system. In: International Conference on Applied Smart Systems (ICASS), pp. 1–6 (2018)Google Scholar
  20. 20.
    Bourdim, S., Hemsas, K.E., Harbouche, Y., Azib, T.: Multi phase stator short-circuit faults diagnosis & classification in DFIG using wavelet & fuzzy based technique. In: 3rd International Conference on Control, Engineering & Information Technology (CEIT), pp. 1–6 (2015)Google Scholar
  21. 21.
    Qiao, W., Lu, D.: A survey on wind turbine condition monitoring and fault diagnosis–Part II: signals and signal processing methods. IEEE Trans. Ind. Electron. 62(10), 6546–6557 (2015) CrossRefGoogle Scholar
  22. 22.
    Sabir, H., Ouassaid, M., Ngote, N.: Diagnosis of rotor winding inter-turn short circuit fault in wind turbine based on DFIG using hybrid TSA/DWT approach. In: 6th International Renewable and Sustainable Energy Conference (IRSEC), pp. 1–6 (2019)Google Scholar
  23. 23.
    Himani, Dahiya, R.: Condition monitoring of wind turbine for rotor fault detection under non stationary conditions. Ain Shams Eng. J. (2017)Google Scholar
  24. 24.
    Sabir, H., Ouassaid, M., Ngote, N.: Diagnosis of rotor winding inter-turn short circuit fault in wind turbine based on DFIG using the TSA-CSA method. In: International Symposium on Advanced Electrical and Communication Technologies (ISAECT), pp. 1–5 (2018)Google Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Engineering for Smart and Sustainable Systems Research Center, Mohammadia School of EngineersMohammed V University in RabatRabatMorocco
  2. 2.ENSMR Engineering SchoolRabatMorocco

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