Diagnosis Methods for Wind Turbine Doubly Fed Induction Generator under Grid Defects

  • Fatima El Hammouchi National Higher School of Electricity and Mechanic, Hassan 2 University
  • Lamiaa El Menzhi National School of Applied Sciences, Abdelmalek Essaadi University
  • Abdallah Saad National Higher School of Electricity and Mechanic, Hassan 2 University

Abstract

In this paper, we present a method developed for diagnosis defects of doubly-fed induction generator DFIG used in wind turbine. Initially, we focus on modeling of a non-defected wind conversion system based on mathematic model created in Matlab Simulink which is able to present the behavior of the wind turbine. Then, in order to increase wind energy performance, we suggest the use of an indirect vector control stator field orientation. Afterwards, we propose a method to diagnose the defects attacking wind turbine generator. This approach is based on frequency spectrum analysis and Lissajous curves of DFIG stator and rotor currents. The DFIG diagnosis defects method is applied to a non-defected generator to have a reliable reference for normal operation of a wind system. However, connected to the grid, wind turbine generator is affected by faults occurring in electrical power networks. For this reason, the proposed diagnosis method is applied also to a defected generator. The resulting Lissajous curves and frequency spectrums are compared to reference data in order to diagnose generator defects type and severity.The simulations had been realized by Matlab Simulink. These results showed the efficiency of the proposed method.
Published
Apr 26, 2019
How to Cite
EL HAMMOUCHI, Fatima; EL MENZHI, Lamiaa; SAAD, Abdallah. Diagnosis Methods for Wind Turbine Doubly Fed Induction Generator under Grid Defects. International Journal of Information Science and Technology, [S.l.], v. 3, n. 3, p. 46 - 55, apr. 2019. ISSN 2550-5114. Available at: <https://innove.org/ijist/index.php/ijist/article/view/79>. Date accessed: 09 aug. 2022.
Section
Special Issue : Optimization and Modeling of Complex Systems