Ontology type: schema:ScholarlyArticle
2019-03
AUTHORSAhmed A. Zaki Diab, Hegazy Rezk
ABSTRACTThe applications of grey wolf (GWO), dragonfly (DFO) and moth–flame (MFA) optimization techniques for optimum sitting of capacitors in various radial distribution systems (RDSs) are presented. The loss sensitivity factor is applied to determine the most candidate buses. Then, each optimization technique is utilized to find optimum placements and sizes of capacitors for determined Buses. In this study, 33-, 69- and 118-bus RDSs are considered for validating the effectiveness and efficiency of studied algorithms. The convergence performance is evaluated for tested RDSs using MATLAB/Simulink software. The obtained results confirm that GWO, DFO and MFA offer accurate convergence to the global minimum point of the objective function with high convergence speed. The ability of the studied techniques for enhancing voltage profiles with considered distribution systems is achieved. Finally, a comparison study between each studied technique with each other and with other techniques like PSO, fuzzy-GA, heuristic, DSA, TLBO, DA-PS, FPA and CSA has been carried out. The parameters of the comparison include: efficiency, execution time, the speed of convergence, minimizing total cost and increasing net savings. The results of comparison indicated that GWO-based algorithm has accurate convergence to optimal location and size of capacitor banks. In addition, it has the best performance in comparison with other techniques. More... »
PAGES1-20
Iranian Journal of Science and Technology, Transactions of Electrical Engineering
ISSUEN/A
VOLUMEN/A
http://scigraph.springernature.com/pub.10.1007/s40998-018-0071-7
DOIhttp://dx.doi.org/10.1007/s40998-018-0071-7
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1105326578
JSON-LD is the canonical representation for SciGraph data.
TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT
[
{
"@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json",
"about": [
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0103",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Numerical and Computational Mathematics",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/01",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Mathematical Sciences",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "Moscow Power Engineering Institute",
"id": "https://www.grid.ac/institutes/grid.77852.3f",
"name": [
"Electrical Engineering Department, Faculty of Engineering, Minia University, Minia, Egypt",
"Electrical Power Systems Department, National Research University, \u201cMPEI\u201d, Moscow, Russian Federation"
],
"type": "Organization"
},
"familyName": "Diab",
"givenName": "Ahmed A. Zaki",
"id": "sg:person.015635556445.21",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015635556445.21"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Minia University",
"id": "https://www.grid.ac/institutes/grid.411806.a",
"name": [
"College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Al-Kharj, Kingdom of Saudi Arabia",
"Electrical Engineering Department, Faculty of Engineering, Minia University, Minia, Egypt"
],
"type": "Organization"
},
"familyName": "Rezk",
"givenName": "Hegazy",
"id": "sg:person.015030210307.27",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015030210307.27"
],
"type": "Person"
}
],
"citation": [
{
"id": "https://doi.org/10.1016/j.ijepes.2011.10.030",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1007232743"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.asej.2016.04.005",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1009043496"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.ijepes.2013.07.011",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1009945249"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.ijepes.2016.01.015",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1012769787"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.ijepes.2014.07.041",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1015620849"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.aej.2016.10.002",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1019998614"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.ijepes.2015.11.059",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1021710034"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.ijepes.2007.08.004",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1022400814"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.knosys.2015.07.006",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1034277407"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.advengsoft.2013.12.007",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1036158139"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1080/15325008.2013.856965",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1036636511"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.rser.2013.10.002",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1038167440"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.enconman.2009.10.016",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1038200119"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.protcy.2016.08.173",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1044258418"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.ijepes.2012.03.006",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1044568300"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.beproc.2011.09.006",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1044579639"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.rser.2013.08.075",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1050344850"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00521-015-1920-1",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1050546953",
"https://doi.org/10.1007/s00521-015-1920-1"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.ijepes.2015.11.085",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1052722293"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1049/iet-gtd.2012.0661",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1056827083"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1049/iet-gtd.2012.0661",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1056827083"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1049/iet-gtd.2013.0290",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1056827180"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1049/iet-gtd.2013.0290",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1056827180"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/61.19265",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1061199187"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/61.25627",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1061199608"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/mepcon.2016.7836929",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1093334126"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/pes.2007.386149",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1095211778"
],
"type": "CreativeWork"
}
],
"datePublished": "2019-03",
"datePublishedReg": "2019-03-01",
"description": "The applications of grey wolf (GWO), dragonfly (DFO) and moth\u2013flame (MFA) optimization techniques for optimum sitting of capacitors in various radial distribution systems (RDSs) are presented. The loss sensitivity factor is applied to determine the most candidate buses. Then, each optimization technique is utilized to find optimum placements and sizes of capacitors for determined Buses. In this study, 33-, 69- and 118-bus RDSs are considered for validating the effectiveness and efficiency of studied algorithms. The convergence performance is evaluated for tested RDSs using MATLAB/Simulink software. The obtained results confirm that GWO, DFO and MFA offer accurate convergence to the global minimum point of the objective function with high convergence speed. The ability of the studied techniques for enhancing voltage profiles with considered distribution systems is achieved. Finally, a comparison study between each studied technique with each other and with other techniques like PSO, fuzzy-GA, heuristic, DSA, TLBO, DA-PS, FPA and CSA has been carried out. The parameters of the comparison include: efficiency, execution time, the speed of convergence, minimizing total cost and increasing net savings. The results of comparison indicated that GWO-based algorithm has accurate convergence to optimal location and size of capacitor banks. In addition, it has the best performance in comparison with other techniques.",
"genre": "research_article",
"id": "sg:pub.10.1007/s40998-018-0071-7",
"inLanguage": [
"en"
],
"isAccessibleForFree": false,
"isPartOf": [
{
"id": "sg:journal.1283955",
"issn": [
"2228-6179",
"2364-1827"
],
"name": "Iranian Journal of Science and Technology, Transactions of Electrical Engineering",
"type": "Periodical"
}
],
"name": "Optimal Sizing and Placement of Capacitors in Radial Distribution Systems Based on Grey Wolf, Dragonfly and Moth\u2013Flame Optimization Algorithms",
"pagination": "1-20",
"productId": [
{
"name": "readcube_id",
"type": "PropertyValue",
"value": [
"c3b6b122c2af94d94dbba51646d539bf2dd12ec02bb4bc9b05f2727303bddabc"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/s40998-018-0071-7"
]
},
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1105326578"
]
}
],
"sameAs": [
"https://doi.org/10.1007/s40998-018-0071-7",
"https://app.dimensions.ai/details/publication/pub.1105326578"
],
"sdDataset": "articles",
"sdDatePublished": "2019-04-10T14:05",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8660_00000494.jsonl",
"type": "ScholarlyArticle",
"url": "http://link.springer.com/10.1007/s40998-018-0071-7"
}
]
Download the RDF metadata as: json-ld nt turtle xml License info
JSON-LD is a popular format for linked data which is fully compatible with JSON.
curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s40998-018-0071-7'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s40998-018-0071-7'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s40998-018-0071-7'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s40998-018-0071-7'
This table displays all metadata directly associated to this object as RDF triples.
143 TRIPLES
21 PREDICATES
50 URIs
17 LITERALS
5 BLANK NODES