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Optimal Tuning for Load Frequency Control Using Ant Lion Algorithm in Multi‐Area Interconnected Power System

Nour EL Yakine Koubaa, Mohamed Menaaa, Kambiz Tehranib, Mohamed Boudoura
a Laboratory of Electrical and Industrial Systems, University of Science and Technology Houari Boumediene, Algiers, Algeria.
b Ecole Supérieure d’ingénieurs, ESIGELEC, Technopole du Madrillet, Rouen, France.
* Corresponding Author: Nour EL Yakine Kouba,

Intelligent Automation & Soft Computing 2019, 25(2), 279-294. https://doi.org/10.31209/2018.100000007

Abstract

This paper presents the use of a novel nature inspired meta-heuristic algorithm namely Ant Lion Optimizer (ALO), which is inspired from the ant lions hunting mechanism to enhance the frequency regulation and optimize the load frequency control (LFC) loop parameters. The frequency regulation issue was formulated as an optimal load frequency control problem (OLFC). The proposed ALO algorithm was applied to reach the best combination of the PID controller parameters in each control area to achieve both frequency and tie-line power flow exchange deviations minimization. The control strategy has been tested firstly with the standard two-area power system, followed by the IEEE threearea Western System Coordinating Council (WSCC) and, lastly, with the large three-area South-Western part of the Mediterranean interconnected power system (SWM): Tunisia, Algeria and Morocco. The dynamic performances of the test systems are compared to other approaches available in literature. The simulation results of this research show that ALO algorithm is able to solve LFC problem and achieve less frequency and tie-line power flow deviations than those determined by other methods used in this paper.

Keywords

Ant Lion Optimizer (ALO); Frequency Regulation; Load Frequency Control (LFC); Optimal Control.

Cite This Article

. , "Optimal tuning for load frequency control using ant lion algorithm in multi‐area interconnected power system," Intelligent Automation & Soft Computing, vol. 25, no.2, pp. 279–294, 2019.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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