ArticleScientific reports2026
Transmission line fault detection and classification using bi-orthogonal wavelet transform (5.5) based signal decomposition.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- A protection strategy for distribution networks using IoT technology and support vector machine.Scientific reports · 2026Article
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8 authors.
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Abstract
The transmission line experiences the highest number of faults in a power system due to its high exposure and the high number of coincidences that might occur along it. This study presents an approach based on transmission line protection using wavelet transform with a bi-orthogonal wavelet 5.5 signal decomposition. The emphasis is on fault identification, and the proposed method studies the concentration of the coefficients and discriminates faulted phases from healthy phases. Up to Level 1, the signal is decomposed using a wavelet to capture the major features of the signal. A comprehensive assessment framework was used, and a 400 kV system was studied as an infinite bus coupled with a 300 km long transmission line. A contrivance for fault recognition is designed to determine anomalies in the transmission line between the faulted phases and their healthy counterparts with considerably higher detailed coefficients. Fault classification is then performed based on the choice of strict and carefully calibrated threshold values, in which faults are precisely identified and classified. A comparative analysis has been carried out with the relevant FFT, DFT and S-transform on the same MATALB platform. The proposed algorithm is also successfully validate for variation in system conditions like current inversion, CT saturation, non-linear effect of MOV and level of series compensation. An inclusive transmission line protection system is reliably and effectively cultivated through the custom of the planned protection procedure, in which it is shown to be highly capable of precisely sensing and categorizing faults. The replication outcomes demonstrate the effectiveness of the system in practical situations and demonstrate that it may be used in real power systems.
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