Classification of the harmonic load types using Multi-Layer Extreme Learning Machine
Aggregates; Classification (of information); Electric power systems; Harmonic analysis; Knowledge acquisition; Learning systems; Auto encoders; Classification results; Electricity consumers; Extreme learning machine; Harmonic loads; Load characteristics; Power system; Power system applications; Elec...
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Institution of Engineering and Technology
2023
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my.uniten.dspace-239942023-05-29T14:54:01Z Classification of the harmonic load types using Multi-Layer Extreme Learning Machine Wong S.Y. Yap K.S. Li X. 55812054100 24448864400 57775484100 Aggregates; Classification (of information); Electric power systems; Harmonic analysis; Knowledge acquisition; Learning systems; Auto encoders; Classification results; Electricity consumers; Extreme learning machine; Harmonic loads; Load characteristics; Power system; Power system applications; Electric power system planning This paper presents a neural network approach intended to aid electricity consumers or power system planner in the task of classification of harmonic load types using the measurements (data samples) collected from one of the power station in Malaysia. In order to allow the classification of the type of harmonic loads, harmonic currents order produced by aggregate harmonic loads and the level of emission are modelled using the Multi-Layer Extreme Learning Machine with autoencoder (hereinafter denoted as ML-ELM-AE). The feasibility of ML-ELM-AE on the classification of the Harmonic empirical data set will be probed, for when the classification results of the Harmonic load types become available, it can come in handy to power system analyst or engineers for analysis in later stage. They can use it to determine the harmonic distortion patterns and to characterize the harmonic currents at network buses. Depending on whether the aggregate load is residential, commercial, or industrial, the load characteristics in terms of its harmonic contents are likely to be different. The achieved results demonstrate the effectiveness of the investigated technique in dealing with the real world power system application of harmonic load type classification, that can be useful in providing good indication to the power system or distribution network planner. � 2018 Institution of Engineering and Technology. All rights reserved. Final 2023-05-29T06:54:01Z 2023-05-29T06:54:01Z 2018 Conference Paper 2-s2.0-85061347432 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061347432&partnerID=40&md5=9b9447c7da3f6b61cbd86701960629b4 https://irepository.uniten.edu.my/handle/123456789/23994 2018 CP749 Institution of Engineering and Technology Scopus |
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Malaysia |
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Universiti Tenaga Nasional |
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| description |
Aggregates; Classification (of information); Electric power systems; Harmonic analysis; Knowledge acquisition; Learning systems; Auto encoders; Classification results; Electricity consumers; Extreme learning machine; Harmonic loads; Load characteristics; Power system; Power system applications; Electric power system planning |
| author2 |
55812054100 |
| author_facet |
55812054100 Wong S.Y. Yap K.S. Li X. |
| format |
Conference Paper |
| author |
Wong S.Y. Yap K.S. Li X. |
| spellingShingle |
Wong S.Y. Yap K.S. Li X. Classification of the harmonic load types using Multi-Layer Extreme Learning Machine |
| author_sort |
Wong S.Y. |
| title |
Classification of the harmonic load types using Multi-Layer Extreme Learning Machine |
| title_short |
Classification of the harmonic load types using Multi-Layer Extreme Learning Machine |
| title_full |
Classification of the harmonic load types using Multi-Layer Extreme Learning Machine |
| title_fullStr |
Classification of the harmonic load types using Multi-Layer Extreme Learning Machine |
| title_full_unstemmed |
Classification of the harmonic load types using Multi-Layer Extreme Learning Machine |
| title_sort |
classification of the harmonic load types using multi-layer extreme learning machine |
| publisher |
Institution of Engineering and Technology |
| publishDate |
2023 |
| _version_ |
1806423322915766272 |
| score |
13.214268 |