Application of Artificial Intelligence to Wind Power Generation: Modelling, Control and Fault Detection

Hadjira Bouazza, Mohamed Lokmane Bendaas, Tayeb Allaoui, Mouloud Denai

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Power converters play a key-role in the grid-integration of wind power generation and as any physical device, they are prone to mal function and failure. There is, therefore, a need for converter health monitoring and fault detection to ensure a reliable and sustainable operation of the wind turbine. This paper presents different artificial intelligence-based fault detection using fuzzy and neuro-fuzzy techniques. The proposed methods are designed for the detection of one or two open-circuit fault in the power switches of the rotor side converter (RSC) of a doubly-fed induction generator (DFIG) wind energy conversion system (WECS). In the proposed detection method only the average values of the three-phase rotor current are used to identify the faulty switch. Alongside these condition monitoring strategies, the paper also present two fuzzy logic-based controllers for the regulation of the real and reactive power flow between the grid and the converter. The performances of the controllers are evaluated under different operating conditions of the power system and the reliability, feasibility and the effectiveness of the proposed fault detection have been verified under various open-switch fault conditions.
Original languageEnglish
Pages (from-to)280-305
Number of pages26
JournalInternational Journal of Intelligent Systems Technologies and Applications (IJISTA)
Issue number3
Publication statusPublished - 26 Jun 2020


  • Wind energy; DFIG; MPPT; fault detection; open-switch fault; type 2 fuzzy logic; ANFIS.
  • MPPT
  • Wind energy
  • Fault detection
  • Maximum power point tracking
  • Type-2 fuzzy logic
  • Open-switch fault
  • Adaptive neuro-fuzzy inference system
  • Doubly-fed induction generation
  • DFIG


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