Detection of Series AC Arc Fault Based on Continuous Wavelet Transform and Artificial Neural Network under Voltage Variation Disturbances

Dimas Okky Anggriawan*, Ardyono Priyadi, Margo Pujiantara, Mauridhi Hery Purnomo

*Corresponding author for this work

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

Abstract

Series arc fault in electrical power system can cause electrical fires. In distribution system, power quality disturbances include voltage variation disturbances often occurs. Voltage variation disturbances can lead to non-stationary waveform, which affect accuracy arc fault detection. To overcome this problem, this paper proposes combination methods of continuous wavelet transform and artificial neural network for detection. Continuous wavelet transform is superior to analyze of non-stationary waveform from series arc fault under voltage variation disturbances. Continuous wavelet transform recognizes series arc fault under voltage variation disturbances by transform of signal to time-frequency domain. Artificial neural network using type of feed forward neural network with Levenberg Marquardt Algorithm for series arc fault identification by data obtained from continuous wavelet transform. Data is trained and tested by artificial neural network. Several waveform model of series arc fault under voltage variation disturbances are selected to tested include normal system, series arc fault under normal condition, series arc fault under voltage sag condition and series arc fault under voltage swell condition. The result show that the algorithm of continuous wavelet transform and artificial neural network have good accuracy for series arc fault detection under normal system, series arc fault, voltage sag condition and voltage swell condition with the accuracy of 99.69 %, 99.2%, 99.97% and 99.9%, respectively.

Original languageEnglish
Title of host publication2024 International Electronics Symposium
Subtitle of host publicationShaping the Future: Society 5.0 and Beyond, IES 2024 - Proceeding
EditorsAndhik Ampuh Yunanto, Afifah Dwi Ramadhani, Yanuar Risah Prayogi, Putu Agus Mahadi Putra, Weny Mistarika Rahmawati, Muhammad Rizani Rusli, Fitrah Maharani Humaira, Faridatun Nadziroh, Nihayatus Sa'adah, Nailul Muna, Aris Bahari Rizki
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages49-53
Number of pages5
ISBN (Electronic)9798350391992
DOIs
Publication statusPublished - 2024
Event26th International Electronics Symposium, IES 2024 - Denpasar, Indonesia
Duration: 6 Aug 20248 Aug 2024

Publication series

Name2024 International Electronics Symposium: Shaping the Future: Society 5.0 and Beyond, IES 2024 - Proceeding

Conference

Conference26th International Electronics Symposium, IES 2024
Country/TerritoryIndonesia
CityDenpasar
Period6/08/248/08/24

Keywords

  • Series arc fault
  • artificial neural network
  • continuous wavelet transform
  • detection
  • voltage variation disturbances

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