https://doi.org/10.1051/epjconf/202533002003
Nonlinear Autoregressive Neural Network Approaches for Managing Active and Reactive Power in DFIG Systems
Laboratory LABSTIC, Department of Industrial and Civil Sciences and Technologies, National School of Applied, Tetouan, Morroco
* Corresponding author: kabira.mjabber@gmail.com
Published online: 30 June 2025
The effective command of the mechanical and electrical components of a wind turbine is essential to secure optimal efficiency and stability of the system. This article aims to present a novel Nonlinear Autoregressive Neural Network (NARNN) strategy for controlling the electrical aspect of a system employing a Doubly-Fed Induction Generator (DFIG). The control strategy is designed to regulate active and reactive power in order to optimise energy production. To generate a reference power signal, rotor speed control is implemented in the mechanical part of the system. The results provided by the presented NARNN control strategy are then compared with those obtained from the reference Proportional Integral (PI) controller.
Key words: neural network predictive / wind turbine / active and reactive power control / nonlinear autoregressive / Indirect PI control / Rotor speed control
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
