Abstract
Proper monitoring of the components of a system leads to a successful control of it. Fault detection and diagnosis (FDD) is an effective methodology that uses system's information commonly measured from sensors and other devices along with mathematical and statistical theories to effectively control and improve a system's performance. In this study, fault detection and diagnosis (iFDD) was applied to analyze and detect series arcing in an AC electrical installation. The iFDD methodology presented in this article focused on pattern recognition and fingerprint analysis of voltage and current signals under induced series arcing. An AC power system, often found in houses or commercial buildings for daily use of electrical appliances and equipment, was constructed and utilized for series arcing experiments. The tests were conducted on experimental currents (2-7 A) measured on domestic loads (halogen lamp and ranger plate). All data were digitized with a sample rate of 250 kHz (oscilloscope sampling rate was 250 MHz). Fingerprints of series arcing under different working conditions were analyzed. Multiresolution wavelet decomposition of current and voltage signals were applied using MATLAB for analysis of fault transients with selection of a suitable mother wavelet.
Original language | English |
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Title of host publication | 2022 5th Asia Conference on Energy and Electrical Engineering, ACEEE 2022 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 69-72 |
Number of pages | 4 |
ISBN (Electronic) | 9781665499330 |
DOIs | |
State | Published - 2022 |
Event | 5th Asia Conference on Energy and Electrical Engineering, ACEEE 2022 - Virtual, Online, Malaysia Duration: 8 Jul 2022 → 10 Jul 2022 |
Publication series
Name | 2022 5th Asia Conference on Energy and Electrical Engineering, ACEEE 2022 |
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Conference
Conference | 5th Asia Conference on Energy and Electrical Engineering, ACEEE 2022 |
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Country/Territory | Malaysia |
City | Virtual, Online |
Period | 8/07/22 → 10/07/22 |
Bibliographical note
Publisher Copyright:© 2022 IEEE.
Keywords
- fault detection and diagnosis
- series arc fault
- signal processing
- wavelet