Transmission loss minimization using SVC based on Particle Swarm Optimization

Abstract

Abstract-This paper describes optimal sizing of static var compensator (SVC) based on Particle Swarm Optimization for minimization of transmission losses considering cost function. Particle Swarm Optimization (PSO) is one of the artificial intelligent search approaches which has the potential to solve such a problem. For this study, static var compensator (SVC) is chosen as the compensation device. Validation through the implementation on the IEEE 26-bus system shows that the PSO is found feasible to achieve the task. The simulations results are compared with those obtained from the Bee Algorithm (BA) technique in the attempt to highlight its merit.

Cluster Identification for Optimal Placement of Static Var Compensator

“…The SVCs is a shunt-connected static var generator or absorber whose output is adjusted to exchange capacitive or inductive so as to maintain or control specific parameters of electrical power system (typically controller reactor). In this paper, the SVC is modeled as a variable shunt reactive susceptance [2], [8], [9][10][11][12][13].…”

“…where r re is the operating rate. The investment cost given in US$/kVar, are determined by the following relations [9][10][11][12][13]…”

“…While, Table IIV tabulates the ranking of SVC installation based on the frequency selectivity of particle buses via PSO and EP techniques. From PSO results, fifteen buses which have higher ranking are buses 30,29,24,21,27,22,16,26,13,14,25,11,12,18, and 28. Besides that, from EP results; fifteen buses were chosen to form the clusters are buses 30, 21, 22, 24, 23, 26, 27, 28, 29, 17, 12, 13, 14, 20, and 25.…”

Optimal Power Flow Problem Solution Incorporating FACTS Devices Using PSO Algorithm

“…The SVC can inject or absorb its reactive power (Q SVC ) at a chosen bus. It injects reactive power into the system Q SVC < 0 and absorbs reactive power from the system if Q SVC > 0 [10]. As an important component for voltage control, it is usually installed at the receiving node of the transmission lines.…”

Analysis of DFIG-STATCOM P2P control action using simulated annealing techniques

“…The literature describes various optimization methods to solve this problem, such as continuous power flow, optimal power flow (OPF) along with effective cost analysis, loss prediction sensitivity analysis, heuristic optimization methods, and stochastic algorithms. This may be further categorise into genetic algorithms (GA), particle swarm optimization (PSO), evolutionary search and harmony search algorithms [32] , [33] .…”