Determination of Different Fault Features in Power Distribution System Based on Wavelet Transform
S H Asman1, N F Ab Aziz2, M Z A Ab. Kadir3, U A Ungku Amirulddin4, M Izadi5
1S H Asman, Institute of Power Engineering (IPE), Universiti Tenaga Nasional, Jalan IKRAM-UNITEN, 43000 Kajang, Selangor, Malaysia.
2N F Ab Aziz, Institute of Power Engineering (IPE), Universiti Tenaga Nasional, Jalan IKRAM-UNITEN, 43000 Kajang, Selangor, Malaysia.
3M Z A Ab. Kadir, Institute of Power Engineering (IPE), Universiti Tenaga Nasional, Jalan IKRAM-UNITEN, 43000 Kajang, Selangor, Malaysia; Advance Lightning, Power and Energy Research Centre (ALPER), University Putra Malaysia,43400, Serdang, Malaysia
4U A Ungku Amirulddin, Institute of Power Engineering (IPE), Universiti Tenaga Nasional, Jalan IKRAM-UNITEN, 43000 Kajang, Selangor, Malaysia.
5M Izadi Institute of Power Engineering (IPE), Universiti Tenaga Nasional, Jalan IKRAM-UNITEN, 43000 Kajang, Selangor, Malaysia.
Manuscript received on November 12, 2019. | Revised Manuscript received on November 25, 2019. | Manuscript published on 30 November, 2019. | PP: 6256-6261 | Volume-8 Issue-4, November 2019. | Retrieval Number: D5104118419/2019©BEIESP | DOI: 10.35940/ijrte.D5104.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Nowadays, there are various signal processing methods that have been studied by many researchers in order to detect faults in power lines. From previous literature, signal processing that works based on time frequency analysis has been proven to accurately detect faults at high speed. In this study, wavelet transform is adopted to analyse fault occurrences on power line of distribution network. Three types of faults due to lightning, switching and short circuit fault were analysed based on their voltage waveform profiles. ‘Daubechies’ 4 (db4) mother wavelet and four levels decomposition were implemented to extract the features. Approximation at level 4 (A4) and detail coefficient at level 1 to 4 (D1-D4) were extracted to evaluate the energy, skewness, and kurtosis. Based on the results, lightning showed the highest energy, skewness and kurtosis compared to the short circuit and switching voltage waveform. Therefore, these features can be utilized as the new parameters for fault detection in a power system network
Keywords: Fault, Features Extraction, Lightning, Wavelet Transform, Distribution Network.
Scope of the Article: Aggregation, Integration, and Transformation.