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An analysis of voltage source inverter switches fault classification using short time Fourier transform

International Journal of Power Electronics and Drive Systems · Vol. 12 No. 4 (2021) · pp. 2209–2220

DOI
10.11591/ijpeds.v12.i4.pp2209-2220
ISSN
2722-256X, 2088-8694
Published
2021-12-01
Publisher
Institute of Advanced Engineering and Science
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

The dependability of power electronics systems, such as three-phase inverters, is critical in a variety of applications. Different types of failures that occur in an inverter circuit might affect system operation and raise the entire cost of the manufacturing process. As a result, detecting and identifying inverter problems for such devices is critical in industry. This study presents the short-time Fourier transform (STFT) for fault classification and identification in three-phase type, voltage source inverter (VSI) switches. TFR represents the signal analysis of STFT, which includes total harmonic distortion, instantaneous RMS current, RMS fundamental current, total non harmonic distortion, total waveform distortion and average current. The features of the faults are used with a rule-based classifier based on the signal parameters to categorise and detect the switch faults. The suggested method's performance is evaluated using 60 signals containing short and open circuit faults with varying characteristics for each switch in VSI. The classification results demonstrate the proposed technique is good to be implemented for VSI switches faults classification, with an accuracy classification rate of 98.3 percent.

Keywords

Collaboration network of the authors

40 authors · 102 co-authorship links · 33 across different organisations · showing the 40 most connected

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universiti Teknikal Malaysia Melaka
  • University of Technology Malaysia
  • University of Osijek
  • Universitas Ahmad Dahlan
  • Islamic Azad University, Tehran
  • Universiti Malaysia Perlis
  • Universiti Sains Malaysia
  • Other organisations
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Auzani JidinUniversiti Teknikal Malaysia Melaka36
Abdul Rahim AbdullahUniversiti Teknikal Malaysia Melaka19
Nik Rumzi Nik IdrisUniversity of Technology Malaysia18
Mohammad JannatiUniversity of Technology Malaysia16
Awang JusohUniversity of Technology Malaysia15
Nik Rumzi Nik IdrisAffiliation unknown15
M. Rahimi YusoffUniversiti Teknikal Malaysia Melaka12
Mohd Junaidi Abdul AzizUniversity of Technology Malaysia10
Kasrul Abdul KarimAffiliation unknown10
Lina HandayaniUniversitas Ahmad Dahlan8
Mohamed SalemUniversiti Sains Malaysia8
Mahmoud ZadehbagheriIslamic Azad University, Tehran8
Goran KneževićUniversity of Osijek6
Mochammad FactaDiponegoro University6
Ljubomir MajdandžićFond za zaštitu okoliša i energetsku učinkovitost5
Mohammad Javad KianiIslamic Azad University, Tehran5
Shahreen KasimAffiliation unknown5
Watra ArsadiandoAffiliation unknown5
Mohammad Faridun Naim TajuddinUniversiti Malaysia Perlis5
Mohd Junaidi Abdul AzizAffiliation unknown5
Hendril Satrian PurnamaAffiliation unknown5
Anggit PamungkasAffiliation unknown5
Zoran BausUniversity of Osijek4
Imam Much Ibnu SubrotoSultan Agung Islamic University4
Dragan MlakićAffiliation unknown4
Emir AlibašićAffiliation unknown4
Muhammad AdoUniversity of Technology Malaysia4
Vahdat NazerianUniversity of Mazandaran4
Muhammad Zaid AihsanUniversiti Malaysia Perlis4
Chibuike Peter OhanuUniversity of Nigeria4
Tri WahonoAffiliation unknown4
Rahim IldarabadiAffiliation unknown4
Azralmukmin AzmiAffiliation unknown4
Shahrin Md AyobAffiliation unknown4

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) http://ijpeds.iaescore.com/index.php/IJPEDS/oai, first harvested 8 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">An analysis of voltage source inverter switches fault classification using short time Fourier transform</dc:title>
	<dc:creator>Manap, Mustafa</dc:creator>
	<dc:creator>Nikolovski, Srete</dc:creator>
	<dc:creator>Skamyin, Aleksandr</dc:creator>
	<dc:creator>Karim, Rony</dc:creator>
	<dc:creator>Sutikno, Tole</dc:creator>
	<dc:creator>Jopri, Mohd Hatta</dc:creator>
	<dc:subject xml:lang="en-US">Distributed Generation, Overcurrent Relay, Microgrid, Fault analysis, Coordination Time</dc:subject>
	<dc:description xml:lang="en-US">The dependability of power electronics systems, such as three-phase inverters, is critical in a variety of applications. Different types of failures that occur in an inverter circuit might affect system operation and raise the entire cost of the manufacturing process. As a result, detecting and identifying inverter problems for such devices is critical in industry. This study presents the short-time Fourier transform (STFT) for fault classification and identification in three-phase type, voltage source inverter (VSI) switches. TFR represents the signal analysis of STFT, which includes total harmonic distortion, instantaneous RMS current, RMS fundamental current, total non harmonic distortion, total waveform distortion and average current. The features of the faults are used with a rule-based classifier based on the signal parameters to categorise and detect the switch faults. The suggested method's performance is evaluated using 60 signals containing short and open circuit faults with varying characteristics for each switch in VSI. The classification results demonstrate the proposed technique is good to be implemented for VSI switches faults classification, with an accuracy classification rate of 98.3 percent.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2021-12-01</dc:date>
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	<dc:identifier>https://ijpeds.iaescore.com/index.php/IJPEDS/article/view/21455</dc:identifier>
	<dc:identifier>10.11591/ijpeds.v12.i4.pp2209-2220</dc:identifier>
	<dc:source xml:lang="en-US">International Journal of Power Electronics and Drive Systems (IJPEDS); Vol 12, No 4: December 2021; 2209-2220</dc:source>
	<dc:source>2722-256X</dc:source>
	<dc:source>2088-8694</dc:source>
	<dc:source>10.11591/ijpeds.v12.i4</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijpeds.iaescore.com/index.php/IJPEDS/article/view/21455/13769</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2021 Mustafa Manap, Srete Nikolovski, Aleksandr Skamyin, Rony Karim, Tole Sutikno, Mohd Hatta Jopri</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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