Semi-Supervised Learning Optimization Based on Generative Models to Identify Type of Electric Load at Low Voltage

Fawaati Tsabita, W. Nur Rohman, Rosmaliati, B. P. Vita Lystianingrum, Mauridhi Hery Purnomo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Electrical energy is a fundamental requirement for a modern society to operate the various electrical equipment used. So that the service providers must always maintain the quality of service, one measure is to maintain the harmonic content in the system to comply with the standards set. Various kinds of electrical appliances are use energy-saving features that cause high harmonic values that can cause damage to the transformer. This study identifies the harmonic value of various types of load combinations. To obtain the load harmonics data, surveys and measurements have been carried out on household consumers served by a distribution transformer. To detect the type of electrical load based on harmonics, semi-supervised learning method is used with generative model algorithm. Method optimization is performed to produce better results from previous studies. This method yields an average of 83.5% accuracy with various experimental scenarios.

Original languageEnglish
Title of host publicationProceeding - 2018 International Seminar on Intelligent Technology and Its Application, ISITIA 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages209-214
Number of pages6
ISBN (Electronic)9781538676547
DOIs
Publication statusPublished - 2 Jul 2018
Event2018 International Seminar on Intelligent Technology and Its Application, ISITIA 2018 - Bali, Indonesia
Duration: 30 Aug 201831 Aug 2018

Publication series

NameProceeding - 2018 International Seminar on Intelligent Technology and Its Application, ISITIA 2018

Conference

Conference2018 International Seminar on Intelligent Technology and Its Application, ISITIA 2018
Country/TerritoryIndonesia
CityBali
Period30/08/1831/08/18

Keywords

  • electrical loads
  • generative models
  • harmonics
  • semi-supervised

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