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https://hdl.handle.net/11499/9357
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Köroğlu, Selim | - |
dc.contributor.author | Demirçali, Akif | - |
dc.date.accessioned | 2019-08-16T13:00:10Z | |
dc.date.available | 2019-08-16T13:00:10Z | |
dc.date.issued | 2016 | - |
dc.identifier.issn | 1532-5008 | - |
dc.identifier.uri | https://hdl.handle.net/11499/9357 | - |
dc.identifier.uri | https://doi.org/10.1080/15325008.2016.1219427 | - |
dc.description.abstract | This article presents an intelligent diagnosis and classification method for power transformer fault classification based on dissolved gas analysis: the support vector machine. It is a powerful algorithm for classification of faults that needs a limited set of small sampling data, a case of applications with non-linear behavior, and a high number of parameters; however, appropriate model parameters must be determined carefully. The selection of parameters has a direct effect on the machine's classification accuracy. In this study, a multi-layer support vector machine classifier is optimized by a grid search method and three heuristic approaches: (1) genetic, (2) differential evolution, and (3) particle swarm optimization algorithms. The performance analysis of the support vector machine hybridized with these optimization methods is demonstrated using the same classification set. The employed structure has five support vector machine layers, each of which uses a Gaussian kernel function due to its advantages of needing one parameter for optimization and providing excellent classification ability for non-linear data. The proposed approach gives highly accurate performance for diagnosis of power transformers. The support vector machine optimized with the particle swarm optimization algorithm has the best accuracy and requires less computational time compared to the other methods. © 2016, Copyright © Taylor & Francis Group, LLC. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Taylor and Francis Inc. | en_US |
dc.relation.ispartof | Electric Power Components and Systems | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | differential evolution algorithm | en_US |
dc.subject | dissolved gas analysis | en_US |
dc.subject | genetic algorithm | en_US |
dc.subject | grid search | en_US |
dc.subject | particle swarm optimization | en_US |
dc.subject | support vector machine | en_US |
dc.subject | transformer fault diagnosis | en_US |
dc.subject | Computer aided diagnosis | en_US |
dc.subject | Evolutionary algorithms | en_US |
dc.subject | Face recognition | en_US |
dc.subject | Fault detection | en_US |
dc.subject | Genetic algorithms | en_US |
dc.subject | Heuristic algorithms | en_US |
dc.subject | Heuristic methods | en_US |
dc.subject | Particle swarm optimization (PSO) | en_US |
dc.subject | Power transformers | en_US |
dc.subject | Support vector machines | en_US |
dc.subject | Vectors | en_US |
dc.subject | Classification accuracy | en_US |
dc.subject | Differential evolution algorithms | en_US |
dc.subject | Dissolved gas analysis | en_US |
dc.subject | Gaussian kernel functions | en_US |
dc.subject | Grid search | en_US |
dc.subject | Particle swarm optimization algorithm | en_US |
dc.subject | Support vector machine classifiers | en_US |
dc.subject | Transformer fault diagnosis | en_US |
dc.subject | Optimization | en_US |
dc.title | Diagnosis of power transformer faults based on multi-layer support vector machine hybridized with optimization methods | en_US |
dc.type | Article | en_US |
dc.identifier.volume | 44 | en_US |
dc.identifier.issue | 19 | en_US |
dc.identifier.startpage | 2172 | |
dc.identifier.startpage | 2172 | en_US |
dc.identifier.endpage | 2184 | en_US |
dc.authorid | 0000-0001-8178-3227 | - |
dc.authorid | 0000-0001-9030-7775 | - |
dc.identifier.doi | 10.1080/15325008.2016.1219427 | - |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.identifier.scopus | 2-s2.0-84994145011 | en_US |
dc.identifier.wos | WOS:000387800200004 | en_US |
dc.identifier.scopusquality | Q2 | - |
dc.owner | Pamukkale University | - |
item.fulltext | No Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.cerifentitytype | Publications | - |
item.languageiso639-1 | en | - |
item.grantfulltext | none | - |
item.openairetype | Article | - |
crisitem.author.dept | 10.04. Electrical-Electronics Engineering | - |
crisitem.author.dept | 10.04. Electrical-Electronics Engineering | - |
Appears in Collections: | Mühendislik Fakültesi Koleksiyonu Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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