Development of Novel Meta-heuristic Algorithms to Optimize the Locations, Type and Ratings of the FACTS Devices in Load Flow Studies for Enhancement of the Power System Operations

Abstract

The current research study probes into the utilization optimization algorithms which are inspired from natural phenomenon for the optimal allocation of FACTS controllers in IEEE bus systems. The main focus is to enhance the system performance by minimizing the active power loss, voltage deviations, and installation cost of FACTS, and maximizing the system loadability. Such study while pursued to determine the location, type, and rating of FACTS controllers by applying the metaheuristic methodologies yielded contributions in definite domains, involving the development of some new variants of existing algorithms. From the viewpoint of algorithms these encompass Jaya blended Moth Flame Optimization technique, augmented Ant Lion Optimization method, Probability Distribution based Salp Swarm Algorithm, and hybrid Ant lion-Moth Flame-Salp Swarm Algorithm which have been very successfully applied to optimal placement of FACTS devices for the first time both in their original and tweaked forms. Moreover, on the other hand, when metaheuristic techniques are being experimented with, the objective function which is optimized, includes single as well as multiple objectives. While the single objectives are modelled to minimize the active power loss and voltage deviations separately, the multiple objective category aims to minimize the active power loss, voltage deviations, and installation cost of FACTS controllers along with the maximization of system loadability. The outcomes acquired by applying the prospective algorithms using both the single and multiple objective formulations are highly profound and motivating. The aforesaid statement can be further strengthened by highlighting some significant quantitative results. The following table gives an insight into the analysis done where the values corresponding to the winning algorithms have been highlighted in bold. Also, the graphical analysis by comparing the performance of the algorithms based on the installation cost of FACTS devices and the system loadability for the same case study as tabulated above has been depicted in the figure below. These deductions have rightfully justified the proposed contributions evolved in the present investigation and the efficiently developed methodologies have surfaced as promising contenders in competition with their counterparts in the metaheuristic optimization realm.