An Artificial-Neural-Network-Based Direct Power Control Approach for Doubly Fed Induction Generators in Wind Power Systems

Chaimae Dardabi (), Santiago Cóbreces Álvarez and Abdelouahed Djebli
Additional contact information
Chaimae Dardabi: Energetic Laboratory, Department of Physics, Abdelmalek Essaadi University, Tetouan 93002, Morocco
Santiago Cóbreces Álvarez: Electronics Department, Alcalá University, 28805 Madrid, Spain
Abdelouahed Djebli: Energetic Laboratory, Department of Physics, Abdelmalek Essaadi University, Tetouan 93002, Morocco

Energies, 2025, vol. 18, issue 8, 1-23

Abstract: The inherent complexity of wind energy systems has necessitated the development of sophisticated control methodologies to optimize operational efficiency. Artificial neural networks (ANN) have emerged as a powerful tool in wind turbine applications, offering sophisticated control capabilities for addressing the intricate challenges of energy conversion. This study focuses on the critical generator control block, where precise power management is essential to maintaining system stability and preventing operational disruptions. This research introduces an innovative ANN-based Direct Power Control (DPC) approach for a Doubly fed induction generator (DFIG) integrated into a wind power system, introducing a dual-MLP approach for precise power regulation. The proposed DPC-ANN controller proved effective in mitigating current ripples and achieving a near-unity power factor, indicating substantial improvement in power quality. Moreover, the spectrum harmonic analysis revealed that the controller yielded the lowest stator current total harmonic distortion of 1.29%, significantly outperforming traditional DPC-PI (2.76%) and DPC-Classic (2.24%) approaches. The proposed technique was rigorously implemented and validated using a real-time simulator (OPAL-RT) and MATLAB/Simulink (2020–2022) environment, specifically tested under a step wind profile. The real-time experimental validation highlights the practical applicability of this approach, bridging the gap between theoretical ANN-based control and real-world wind energy system implementation. These findings reinforce the potential of intelligent control strategies for optimizing renewable energy technologies, paving the way for more efficient and adaptive wind turbine control solutions.

Keywords: artificial neural networks (ANNs); direct power control (DPC); doubly fed induction generators (DFIG); real-time simulation (RTS); total harmonic distortion (THD); wind power (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/8/1989/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/8/1989/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:8:p:1989-:d:1633576

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().