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A comparative study of multi-objective optimal power flow based on particle swarm, evolutionary programming, and genetic algorithm

This paper compares the performance of three population-based algorithms including particle swarm optimization (PSO), evolutionary programming (EP), and genetic algorithm (GA) to solve the multi-objective optimal power flow (OPF) problem. The unattractive characteristics of the cost-based OPF includ...

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Main Authors:	Kahourzade, S., Mahmoudi, A., Mokhlis, Hazlie
Format:	Article
Language:	English
Published:	2015
Subjects:	T Technology (General) TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://eprints.um.edu.my/13944/1/A_comparative_study_of_multi-objective_optimal_power_flow_based.pdf http://eprints.um.edu.my/13944/ http://link.springer.com/article/10.1007/s00202-014-0307-0
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spelling	my.um.eprints.139442019-10-24T08:07:49Z http://eprints.um.edu.my/13944/ A comparative study of multi-objective optimal power flow based on particle swarm, evolutionary programming, and genetic algorithm Kahourzade, S. Mahmoudi, A. Mokhlis, Hazlie T Technology (General) TK Electrical engineering. Electronics Nuclear engineering This paper compares the performance of three population-based algorithms including particle swarm optimization (PSO), evolutionary programming (EP), and genetic algorithm (GA) to solve the multi-objective optimal power flow (OPF) problem. The unattractive characteristics of the cost-based OPF including loss, voltage profile, and emission justifies the necessity of multi-objective OPF study. This study presents the programming results of the nine essential single-objective and multi-objective functions of OPF problem. The considered objective functions include cost, active power loss, voltage stability index, and emission. The multi-objective optimizations include cost and active power loss, cost and voltage stability index, active power loss and voltage stability index, cost and emission, and finally cost, active power loss, and voltage stability index. To solve the multi-objective OPF problem, Pareto optimal method is used to form the Pareto optimal set. A fuzzy decision-based mechanism is applied to select the best comprised solution. In this work, to decrease the running time of load flow calculation, a new approach including combined Newton-Raphson and Fast-Decouple is conducted. The proposed methods are tested on IEEE 30-bus test system and the best method for each objective is determined based on the total cost and the convergence values of the considered objectives. The programming results indicate that based on the inter-related nature of the objective functions, a control system cannot be recommended based on individual optimizations and the secondary criteria should also be considered. 2015-03 Article PeerReviewed application/pdf en http://eprints.um.edu.my/13944/1/A_comparative_study_of_multi-objective_optimal_power_flow_based.pdf Kahourzade, S. and Mahmoudi, A. and Mokhlis, Hazlie (2015) A comparative study of multi-objective optimal power flow based on particle swarm, evolutionary programming, and genetic algorithm. Electrical Engineering, 97 (1). pp. 1-12. ISSN 0948-7921 http://link.springer.com/article/10.1007/s00202-014-0307-0 DOI 10.1007/s00202-014-0307-0
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
language	English
topic	T Technology (General) TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	T Technology (General) TK Electrical engineering. Electronics Nuclear engineering Kahourzade, S. Mahmoudi, A. Mokhlis, Hazlie A comparative study of multi-objective optimal power flow based on particle swarm, evolutionary programming, and genetic algorithm
description	This paper compares the performance of three population-based algorithms including particle swarm optimization (PSO), evolutionary programming (EP), and genetic algorithm (GA) to solve the multi-objective optimal power flow (OPF) problem. The unattractive characteristics of the cost-based OPF including loss, voltage profile, and emission justifies the necessity of multi-objective OPF study. This study presents the programming results of the nine essential single-objective and multi-objective functions of OPF problem. The considered objective functions include cost, active power loss, voltage stability index, and emission. The multi-objective optimizations include cost and active power loss, cost and voltage stability index, active power loss and voltage stability index, cost and emission, and finally cost, active power loss, and voltage stability index. To solve the multi-objective OPF problem, Pareto optimal method is used to form the Pareto optimal set. A fuzzy decision-based mechanism is applied to select the best comprised solution. In this work, to decrease the running time of load flow calculation, a new approach including combined Newton-Raphson and Fast-Decouple is conducted. The proposed methods are tested on IEEE 30-bus test system and the best method for each objective is determined based on the total cost and the convergence values of the considered objectives. The programming results indicate that based on the inter-related nature of the objective functions, a control system cannot be recommended based on individual optimizations and the secondary criteria should also be considered.
format	Article
author	Kahourzade, S. Mahmoudi, A. Mokhlis, Hazlie
author_facet	Kahourzade, S. Mahmoudi, A. Mokhlis, Hazlie
author_sort	Kahourzade, S.
title	A comparative study of multi-objective optimal power flow based on particle swarm, evolutionary programming, and genetic algorithm
title_short	A comparative study of multi-objective optimal power flow based on particle swarm, evolutionary programming, and genetic algorithm
title_full	A comparative study of multi-objective optimal power flow based on particle swarm, evolutionary programming, and genetic algorithm
title_fullStr	A comparative study of multi-objective optimal power flow based on particle swarm, evolutionary programming, and genetic algorithm
title_full_unstemmed	A comparative study of multi-objective optimal power flow based on particle swarm, evolutionary programming, and genetic algorithm
title_sort	comparative study of multi-objective optimal power flow based on particle swarm, evolutionary programming, and genetic algorithm
publishDate	2015
url	http://eprints.um.edu.my/13944/1/A_comparative_study_of_multi-objective_optimal_power_flow_based.pdf http://eprints.um.edu.my/13944/ http://link.springer.com/article/10.1007/s00202-014-0307-0
_version_	1648736131927244800
score	13.211869

A comparative study of multi-objective optimal power flow based on particle swarm, evolutionary programming, and genetic algorithm

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