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Design and development of PI controller for DFIG grid integration using neural tuning method ensembled with dense plexus terminals.
Hete, R R; Shrivastava, Tarun; Dash, Ritesh; Anupallavi, L; Fathima, Misba; Reddy, K Jyotheeswara; Dhanamjayalu, C; Mohammad, Faruq; Khan, Baseem.
Afiliación
  • Hete RR; Department of Electrical Engineering, G.H.Raisoni University, Amravati, India.
  • Shrivastava T; Department of Electrical Engineering, G. H. Raisoni Institute of Engineering and Technology, Nagpur, India.
  • Dash R; School of Electrical and Electronics Engineering, REVA University, Bangalore, India.
  • Anupallavi L; School of Electrical and Electronics Engineering, REVA University, Bangalore, India.
  • Fathima M; School of Electrical and Electronics Engineering, REVA University, Bangalore, India.
  • Reddy KJ; School of Electrical and Electronics Engineering, REVA University, Bangalore, India.
  • Dhanamjayalu C; School of Electrical Engineering, Vellore Institute of Technology, Vellore, India. dhanush403@gmail.com.
  • Mohammad F; Department of Chemistry, College of Science, King Saud University, P.O. Box 2455, 11451, Riyadh, Kingdom of Saudi Arabia.
  • Khan B; Department of Electrical and Computer Engineering, Hawassa University, Hawassa 05, Ethiopia. baseemkh@hu.edu.et.
Sci Rep ; 14(1): 7916, 2024 Apr 04.
Article en En | MEDLINE | ID: mdl-38575667
ABSTRACT
In a DFIG grid interconnected system, the control of real and reactive power relies on various factors. This paper presents an approach to regulate the flow of real and reactive power using a Neural Tuning Machine (NTM) based on a recurrent neural network. The focus is on controlling the flow of reactive power from the rotor-side converter, which is proportional to the grid-side controller through a coupling voltage. The proposed NTM method leverages neural networks to fine-tune the parameters of the PI controller, optimizing performance for DFIG grid integration. By integrating dense plexus terminals, also known as dense connections, within the neural network, the control system achieves enhanced adaptability, robustness, and nonlinear dynamics, addressing the challenges of the grid. Grid control actions are based on the voltage profile at different bus locations, thereby regulating voltage. This article meticulously examines the analysis in terms of DFIG configuration and highlights the advantages of the neural tuning machine in controlling inner current loop parameters compared to conventional PI controllers. To demonstrate the robustness of the control algorithm, a MATLAB Simulink model is designed, and validation is conducted with three different benchmarking models. All calculations and results presented in the article strictly adhere to IEEE and IEC standards. This research contributes to advancing control methodologies for DFIG grid integration and lays the groundwork for further exploration of neural tuning methods in power system control.

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Sci Rep Año: 2024 Tipo del documento: Article País de afiliación: India

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Sci Rep Año: 2024 Tipo del documento: Article País de afiliación: India