A new hybrid Nelder-Mead particle swarm optimization for coordination optimization of directional overcurrent relays
type: Item
instance of: scholarly article
title: A new hybrid Nelder-Mead particle swarm optimization for coordination optimization of directional overcurrent relays
zbMATH Open document ID: 1264.90180
DOI: 10.1155/2012/456047
author: An Liu
author: Ming-Ta Yang
published in: Mathematical Problems in Engineering
publication date: 2013-06-11T00:00:00Z
review text: Summary: Coordination optimization of directional overcurrent relays (DOCRs) is an important part of an efficient distribution system. This optimization problem involves obtaining the time dial setting (TDS) and pickup current (\(I_p\)) values of each DOCR. The optimal results should have the shortest primary relay operating time for all fault lines. Recently, the particle swarm optimization (PSO) algorithm has been considered an effective tool for linear/nonlinear optimization problems with application in the protection and coordination of power systems. With a limited runtime period, the conventional PSO considers the optimal solution as the final solution, and an early convergence of PSO results in decreased overall performance and an increase in the risk of mistaking local optima for global optima. Therefore, this study proposes a new hybrid Nelder-Mead simplex search method and particle swarm optimization (proposed NM-PSO) algorithm to solve the DOCR coordination optimization problem. PSO is the main optimizer, and the Nelder-Mead simplex search method is used to improve the efficiency of PSO due to its potential for rapid convergence. To validate the proposal, this study compared the performance of the proposed algorithm with that of PSO and original NM-PSO. The findings demonstrate the outstanding performance of the proposed NM-PSO in terms of computation speed, rate of convergence, and feasibility.
Mathematics Subject Classification ID: 90C53
Mathematics Subject Classification ID: 65K05
zbMATH DE Number: 6173294
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2012/456047
OpenAlex ID: W2102080301
Wikidata QID: Q58911677
cites work: Constraint handling in genetic algorithms using a gradient-based repair method
cites work: An application of swarm optimization to nonlinear programming
cites work: Combining biometric fractal pattern and particle swarm optimization-based classifier for fingerprint recognition
description: scientific article; zbMATH DE number 6173294