Classification of the harmonic load types using Multi-Layer Extreme Learning Machine

S. Y. Wong, Keem Siah Yap, X. Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationIET Conference Publications
PublisherInstitution of Engineering and Technology
EditionCP749
ISBN (Electronic)9781785618161, 9781785618437, 9781785618468, 9781785618871, 9781785619427, 9781785619694, 9781839530036
ISBN (Print)9781785617911
Publication statusPublished - 01 Jan 2018
Event5th IET International Conference on Clean Energy and Technology, CEAT 2018 - Kuala Lumpur, Malaysia
Duration: 05 Sep 201806 Sep 2018

Publication series

NameIET Conference Publications
NumberCP749
Volume2018

Conference

Conference5th IET International Conference on Clean Energy and Technology, CEAT 2018
CountryMalaysia
CityKuala Lumpur
Period05/09/1806/09/18

Fingerprint

Learning systems
Harmonic distortion
Electric power distribution
Electricity
Neural networks
Engineers

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Cite this

Wong, S. Y., Yap, K. S., & Li, X. (2018). Classification of the harmonic load types using Multi-Layer Extreme Learning Machine. In IET Conference Publications (CP749 ed.). (IET Conference Publications; Vol. 2018, No. CP749). Institution of Engineering and Technology.
Wong, S. Y. ; Yap, Keem Siah ; Li, X. / Classification of the harmonic load types using Multi-Layer Extreme Learning Machine. IET Conference Publications. CP749. ed. Institution of Engineering and Technology, 2018. (IET Conference Publications; CP749).
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Wong, SY, Yap, KS & Li, X 2018, Classification of the harmonic load types using Multi-Layer Extreme Learning Machine. in IET Conference Publications. CP749 edn, IET Conference Publications, no. CP749, vol. 2018, Institution of Engineering and Technology, 5th IET International Conference on Clean Energy and Technology, CEAT 2018, Kuala Lumpur, Malaysia, 05/09/18.

Classification of the harmonic load types using Multi-Layer Extreme Learning Machine. / Wong, S. Y.; Yap, Keem Siah; Li, X.

IET Conference Publications. CP749. ed. Institution of Engineering and Technology, 2018. (IET Conference Publications; Vol. 2018, No. CP749).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Wong SY, Yap KS, Li X. Classification of the harmonic load types using Multi-Layer Extreme Learning Machine. In IET Conference Publications. CP749 ed. Institution of Engineering and Technology. 2018. (IET Conference Publications; CP749).