<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper presents an experimental implementation of a Neural Sliding Mode Linearization approach for the control of a double-fed induction generator connected to an infinite bus via transmission lines. The rotor windings are connected to the grid via a back-to-back converter, while the stator windings are directly coupled to the network. The chosen control scheme is applied to obtain the required stator power trajectories by controlling the rotor currents and to track the desired values of the DC-link output voltage and the grid power factor. This controller is based on a neural identifier trained online using an Extended Kalman Filter. Based on such identifier, an adequate model is obtained, which is used for synthesizing the required controllers. The proposed control scheme is experimentally verified on 1/4 HP DFIG prototype considering normal and abnormal grid conditions. In addition, maximum power extraction from a random wind profile is tested in the presence of different grid scenarios. Moreover, a comparison with conventional control schemes is performed. The obtained results illustrate the capability of the proposed control scheme to achieve active power, reactive power, and DC voltage desired trajectories tracking and to operate the wind power system even in the presence of parameter variation and grid disturbances, which helps to ensure the stability of the system and improve generated power quality.</p>
</abstract>

This paper presents an experimental implementation of a Neural Sliding Mode Linearization approach for the control of a double-fed induction generator connected to an infinite bus via transmission lines. The rotor windings are connected to the grid via a back-to-back converter, while the stator windings are directly coupled to the network. The chosen control scheme is applied to obtain the required stator power trajectories by controlling the rotor currents and to track the desired values of the DC-link output voltage and the grid power factor. This controller is based on a neural identifier trained online using an Extended Kalman Filter. Based on such identifier, an adequate model is obtained, which is used for synthesizing the required controllers. The proposed control scheme is experimentally verified on 1/4 HP DFIG prototype considering normal and abnormal grid conditions. In addition, maximum power extraction from a random wind profile is tested in the presence of different grid scenarios. Moreover, a comparison with conventional control schemes is performed. The obtained results illustrate the capability of the proposed control scheme to achieve active power, reactive power, and DC voltage desired trajectories tracking and to operate the wind power system even in the presence of parameter variation and grid disturbances, which helps to ensure the stability of the system and improve generated power quality.

Real-time Neural Sliding Mode Linearization Control for a Doubly Fed Induction Generator under Disturbances

Power Electronics and Drives

This work is licensed under the Creative Commons Attribution 4.0 International License.

This paper presents an experimental implementation of a Neural Sliding Mode Linearization approach for the control of a double-fed induction generator...

Real-time Neural Sliding Mode Linearization Control for a Doubly Fed Induction Generator under Disturbances

Pubblicato online: 13 apr 2024

Pagine: 238 - 256

Ricevuto: 26 gen 2024

Accettato: 10 mar 2024

DOI: https://doi.org/10.2478/pead-2024-0016

Parole chiave
Doubly Fed Induction Generator, Real-Time, Sliding Mode, Neural Linearization, Neural Networks, Wind Turbine

© 2024 Larbi Djilali et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Real-time Neural Sliding Mode Linearization Control for a Doubly Fed Induction Generator under Disturbances

Pubblicato online: 13 apr 2024

Pagine: 238 - 256

Ricevuto: 26 gen 2024

Accettato: 10 mar 2024

DOI: https://doi.org/10.2478/pead-2024-0016

Parole chiaveDoubly Fed Induction Generator, Real-Time, Sliding Mode, Neural Linearization, Neural Networks, Wind Turbine

© 2024 Larbi Djilali et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Doubly Fed Induction Generator, Real-Time, Sliding Mode, Neural Linearization, Neural Networks, Wind Turbine