International Journal of Electrical and Computer Engineering
Vol 7, No 3: June 2017

Wind Farm Management using Artificial Intelligent Techniques

Boualam Benlahbib (Laboratoire d'
Instrumentation, Faculté d'
Electronique et d'
Informatique USTHB, Alger (Algérie))

Farid Bouchafaa (Laboratoire d'
Instrumentation, Faculté d'
Electronique et d'
Informatique USTHB, Alger (Algérie))

Saad Mekhilef (University of Malaya, Malaysia)
Noureddine Bouarroudj (Unité de Recherche Appliquée en Energies Renouvelables, URAER, Centre de Développement des Energies Renouvelables)



Article Info

Publish Date
01 Jun 2017

Abstract

This paper presents a comparative study between genetic algorithm and particle swarm optimization methods to determine the optimal proportional–integral (PI) controller parameters for a wind farm management algorithm. This study primarily aims to develop a rapid and stable system by tuning the PI controller, thus providing excellent monitoring for a wind farm system. The wind farm management system supervises the active and reactive power of the wind farm by sending references to each wind generator. This management system ensures that all wind generators achieve their required references. Furthermore, the entire management is included in the normal controlling power set points of the wind farm as designed by a central control system. The performance management of this study is tested through MATLAB/Simulink simulation results for the wind farm based on three doublyfed induction generators

Copyrights © 2017






Journal Info

Abbrev

IJECE

Publisher

Subject

Computer Science & IT Electrical & Electronics Engineering

Description

International Journal of Electrical and Computer Engineering (IJECE, ISSN: 2088-8708, a SCOPUS indexed Journal, SNIP: 1.001; SJR: 0.296; CiteScore: 0.99; SJR & CiteScore Q2 on both of the Electrical & Electronics Engineering, and Computer Science) is the official publication of the Institute of ...