This paper presents a novel method for controlling a Doubly Fed Induction Generator (DFIG) wind energy system using a hybrid controller combining an Artificial Neural Network (ANN) and a Brain Emotional Learning Based Intelligent Controller (BELBIC). The BELBIC element mimics human decision-making to extract insights from the system's dynamic behavior, which then trains the ANN to adapt and learn over time. This BELBIC-based ANN controller demonstrates superior capabilities in handling unexpected disturbances and variations compared to traditional PI controllers. The study evaluates the proposed method under scenarios changing wind speeds, voltage fluctuations. Extensive simulations show that the DFIG system with this hybrid controller achieves improved power output regulation and grid stability, highlighting the effectiveness and promising prospects of this approach for wind energy system control.


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    Title :

    Artificial Neural Network Controller for Doubly Fed Induction Generator-Based Wind Energy Conversion System


    Contributors:


    Publication date :

    2024-07-31


    Size :

    651446 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English