Hybridized symbiotic organism search algorithm for the optimal operation of directional overcurrent relays
type: Item
instance of: scholarly article
title: Hybridized symbiotic organism search algorithm for the optimal operation of directional overcurrent relays
zbMATH Open document ID: 1390.93594
DOI: 10.1155/2018/4605769
author: Muhammad Sulaiman
author: Ashfaq Ahmad
author: Asfandyar Khan
author: Shakoor Muhammad
published in: Complexity
publication date: 2018-06-25T00:00:00Z
review text: Summary: This paper presents the solution of Directional OverCurrent Relay (DOCR) problems using Simulated Annealing based Symbiotic Organism Search (SASOS). The objective function of the problem is to minimize the sum of the operating times of all primary relays. The DOCR problem is nonlinear and highly constrained with two types of decision variables, namely, the Time Dial Settings (TDS) and Plug Setting (PS). In this paper, three models of the problem are considered, the IEEE 3-bus, 4-bus, and 6-bus, respectively. We have applied SASOS to solve the problem and the obtained results are compared with other algorithms available in the literature.
Mathematics Subject Classification ID: 93C95
Mathematics Subject Classification ID: 93B40
Mathematics Subject Classification ID: 93A30
zbMATH DE Number: 6893962
zbMATH Keywords: directional overcurrent relay (DOCR) problems
zbMATH Keywords: simulated annealing
zbMATH Keywords: Symbiotic Organism Search (SASOS)
zbMATH Keywords: decision variables
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2018/4605769
OpenAlex ID: W2784519930
cites work: Improving power system stability with Gramian matrix-based optimal setting of a single series FACTS device: feasibility study in Vietnamese power system
cites work: New mutation schemes for differential evolution algorithm and their application to the optimization of directional over-current relay settings
cites work: Structural evaluation for distribution networks with distributed generation based on complex network
cites work: Smart microgrid energy management using a novel artificial shark optimization
description: scientific article; zbMATH DE number 6893962