Swarm intelligence-based optimization of FACTS devices: a review of operation, control and emerging algorithms

Abstract

The integration of Flexible AC Transmission System (FACTS) devices into modern power networks plays a pivotal role in enhancing voltage stability, reducing transmission losses, and improving overall power transfer capability. Determining the optimal location and sizing of these devices is a critical task that significantly influences system performance. In recent years, swarm intelligence (SI) algorithms have emerged as powerful optimization tools for addressing such complex, nonlinear, and multi-objective problems in power systems. This study presents a comprehensive review of the application of swarm intelligence techniques, Artificial Bee Colony (ABC), Bacterial Foraging Optimization (BFO), Dragonfly Algorithm (DA), Salp Swarm Algorithm (SSA), and Particle Swarm Optimization (PSO). These algorithms are used to optimize the placement and sizing of FACTS devices, such as Static Var Compensators (SVCs), Thyristor-Controlled Series Capacitors (TCSCs), and Static Synchronous Compensators (STATCOMs). The review highlights the underlying mechanisms, strengths, and limitations by comparing the performance of each algorithm in terms of convergence, optimal location, and sizing of a particular FACT device in a power transfer system to enhance voltage stability, minimize real power losses, and improve system loadability. The review provides a comprehensive resource for researchers and practitioners interested in applying swarm intelligence-based optimization techniques of FACTS devices in power transmission systems.