dc.contributor.authors |
Yilmaz, Alper; Kucuker, Ahmet; Bayrak, Gokay |
|
dc.date.accessioned |
2022-12-20T13:24:55Z |
|
dc.date.available |
2022-12-20T13:24:55Z |
|
dc.date.issued |
2022 |
|
dc.identifier.issn |
0360-3199 |
|
dc.identifier.uri |
http://dx.doi.org/10.1016/j.ijhydene.2022.02.0330360-3199 |
|
dc.identifier.uri |
https://hdl.handle.net/20.500.12619/99095 |
|
dc.description |
Bu yayının lisans anlaşması koşulları tam metin açık erişimine izin vermemektedir. |
|
dc.description.abstract |
In this study, a new hybrid machine learning (ML) method is developed to classify the power quality disturbances (PQDs) for a hydrogen energy-based distributed generator (DG) system. The proposed hybrid ML method uses a new approach for the feature extraction by using a pyramidal algorithm with an un-decimated wavelet transform (UWT). The pyramidal UWT method is used and investigated with the Stochastic Gradient Boosting Trees (SGBT) classifier to classify PQD signals for a Solid Oxide Fuel Cell & Photovoltaic (SOFC&PV)-based DG. The overfitting problem of SGBT in noisy signals is eliminated with the features extracted by pyramidal UWT. Mathematical, simulative and real data results confirm that the developed UWT-SGBT method can classify PQDs with high accuracy of up to 99.59%. The proposed method is also tested under noisy conditions, and the pyramidal UWT-SGBT method outperformed other ML with wavelet transform (WT)-based methods in the literature in terms of noise immunity. (c) 2022 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved. |
|
dc.language |
English |
|
dc.language.iso |
eng |
|
dc.relation.isversionof |
10.1016/j.ijhydene.2022.02.0330360-3199 |
|
dc.subject |
Chemistry |
|
dc.subject |
Electrochemistry |
|
dc.subject |
Energy & Fuels |
|
dc.subject |
Hydrogen energy |
|
dc.subject |
Power quality |
|
dc.subject |
Machine learning |
|
dc.subject |
SOFC |
|
dc.subject |
Distributed generation |
|
dc.title |
Automated classification of power quality disturbances in a SOFC&PV-based distributed generator using a hybrid machine learning method with high noise immunity |
|
dc.contributor.authorID |
Yılmaz, Alper/0000-0003-3736-3668 |
|
dc.contributor.authorID |
Küçüker, Ahmet/0000-0001-9412-5223 |
|
dc.identifier.volume |
47 |
|
dc.identifier.startpage |
19797 |
|
dc.identifier.endpage |
19809 |
|
dc.relation.journal |
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY |
|
dc.identifier.issue |
45 |
|
dc.identifier.doi |
10.1016/j.ijhydene.2022.02.0330360-3199 |
|
dc.identifier.eissn |
1879-3487 |
|
dc.contributor.author |
Yilmaz, Alper |
|
dc.contributor.author |
Kucuker, Ahmet |
|
dc.contributor.author |
Bayrak, Gokay |
|
dc.relation.publicationcategory |
Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı |
|