e-ISSN : 0975-4024 p-ISSN : 2319-8613   
CODEN : IJETIY    

International Journal of Engineering and Technology

Home
IJET Topics
Call for Papers 2021
Author Guidelines
Special Issue
Current Issue
Articles in Press
Archives
Editorial Board
Reviewer List
Publication Ethics and Malpractice statement
Authors Publication Ethics
Policy of screening for plagiarism
Open Access Statement
Terms and Conditions
Contact Us

ABSTRACT

ISSN: 0975-4024

Title : Optimal sizing and placement of Static and Dynamic VAR devices through Imperialist Competitive Algorithm for minimization of Transmission Power Loss
Authors : Pramod Kumar Gouda, P K Hota, K. Chandrasekar
Keywords : VAR sources, Transmission Power Loss, Imperialist Competitive Algorithm, Particle Swarm Optimization, Genetic Algorithm
Issue Date : Feb - Mar 2014
Abstract :
This paper presents the applications of static and dynamic VAR sources for Transmission Power Loss (TPL) minimization using Imperialist Competitive Algorithm (ICA). Static VAR sources consists of switchable shunt capacitors whereas, the dynamic VAR sources are flexible AC transmission system (FACTS) devices. A novel approach of simultaneous optimal placement and sizing of static and dynamic VAR sources has been proposed which proves to be more efficient in TPL minimization when compared to their individual counter parts. Usage of static and dynamic VAR sources simultaneously makes the power system optimization problem more complex, which needs special optimization tool. Hence, a novel ICA optimization algorithm is also proposed in this paper to achieve a global optimization solution for the above mentioned complex problem. The proposed ICA is inspired by imperialistic competition in which all the countries are divided into two types: imperialist states and colonies. Imperialistic competition is the main part of proposed algorithm and hopefully causes the colonies to converge to the global minimum of the optimization problem. The proposed method is tested on the standard IEEE-14 bus and IEEE-118 bus test systems. Results obtained are compared against the individual usage of VAR sources and as well as with the other proven optimization algorithms such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Results indicate that the proposed method obtains a better optimal solution when compared to that of the conventional approaches.
Page(s) : 333-342
ISSN : 0975-4024
Source : Vol. 6, No.1