Open Access

ARTICLE

Optimal Location, Sizing and Technology Selection of STATCOM for Power Loss Minimization and Voltage Profile Using Multiple Optimization Methods

Hajer Hafaiedh^1,2, Adel Mahjoub³, Yahia Saoudi⁴, Anouar Benamor², Okba Taouali^5,*, Kamel Zidi⁶, Wad Ghaban⁶

1 Department of Electrical Engineering, Higher National School of Engineering of Tunis (ENSIT), University of Tunis, Tunis, 1008, Tunisia
2 Department of Electrical Engineering, Laboratory of Automatic, Signal and Image Processing (LARATSI), National School of Engineers of Monastir (ENIM), University of Monastir, Monastir, 5019, Tunisia
3 Department of Electrical Engineering, Higher Institute of Applied Science and Technology of Kairouan (ISSAT Kairouan), University of Kairouan, Kairouan, 3100, Tunisia
4 Department of Electrical Engineering, Laboratory of Control and Energy Management, National Engineering School of Sfax, University of Sfax, Sfax, 3038, Tunisia
5 Faculty of Computers and Information Technology, University of Tabuk, Tabuk, 71491, Saudi Arabia
6 Applied College, University of Tabuk, Tabuk, 71491, Saudi Arabia

* Corresponding Author: Okba Taouali. Email:

Computer Modeling in Engineering & Sciences 2025, 145(1), 571-596. https://doi.org/10.32604/cmes.2025.071642

Received 09 August 2025; Accepted 09 October 2025; Issue published 30 October 2025

Abstract

Several optimization methods, such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), are used to select the most suitable Static Synchronous Compensator (STATCOM) technology for the optimal operation of the power system, as well as to determine its optimal location and size to minimize power losses. An IEEE 14 bus system, integrating three wind turbines based on Squirrel Cage Induction Generators (SCIGs), is used to test the applicability of the proposed algorithms. The results demonstrate that these algorithms are capable of selecting the most appropriate technology while optimally sizing and locating the STATCOM to reduce power losses in the network. Specifically, the optimized STATCOM allocation using the Particle Swarm Optimization (PSO) achieves a 7.44% reduction in total active power loss compared to the optimized allocation using the Genetic Algorithm (GA). Furthermore, the voltage magnitudes at buses 4, 9, and 10, which initially had exceeded the upper voltage limit, were reduced and brought within acceptable ranges, thereby improving the system’s overall voltage profile. Consequently, the optimal allocation of the STATCOM significantly enhances the efficiency and performance of the power network.

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Keywords

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