A New Fractional-Order Adaptive Interval Type-3 Fuzzy Logic-Backstepping Control Algorithm of Doubly Fed Induction Generators | Zendy

Tarek Aounallah | Zendy; Nawal Ferhat | Zendy; Najib Essounbouli | Zendy; Abdelaziz Hamzaoui | Zendy

Open Access

A New Fractional-Order Adaptive Interval Type-3 Fuzzy Logic-Backstepping Control Algorithm of Doubly Fed Induction Generators

Author(s) -

Tarek Aounallah,

Nawal Ferhat,

Najib Essounbouli,

Abdelaziz Hamzaoui

Publication year - 2025

Publication title -

ieee access

Language(s) - English

Resource type - Magazines

SCImago Journal Rank - 0.587

H-Index - 127

eISSN - 2169-3536

DOI - 10.1109/access.2025.3575048

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

This paper presents a new fractional-order control algorithm for a doubly fed induction generator-based wind energy conversion system. Nonlinearities resulting from wind turbine dynamics, parametric uncertainties due to generator modeling, and stochastic wind disturbances can seriously affect control performances and consequently decrease wind power extraction. The suggested approach combines interval type 3 adaptive fuzzy systems with a backstepping control technique to improve the quality of the closed-loop response and the robustness of the system. Besides approximating nonlinear functions, interval type 3 fuzzy systems are used to handle a higher level of uncertainty compared to type 1 or type 2 fuzzy logic approaches. The stability analysis is proved according to the Lyapunov method extended to fractional order. Several simulation results are presented accompanied by a comparative study with some other well-known fuzzy backstepping methods.

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