Detection and Classification of Transmission Line Faults Using Stockwell Transform and Rule Based Decision Tree

Sharma, Gajanand; Mahela, Om Prakash; Kumar, M. Ravi; Kumar, Neeraj

doi:10.1109/icsedps.2018.8536006

Cited by 12 publications

(2 citation statements)

References 6 publications

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“…Many attempts to obtain the best protection systems are based on altering the system stability and incorporating many realistic variables [9][10][11][12][13][14][15][16][17]. In addition, the hurdles and difficulties associated with such systems to achieve efficient faults detection increases with the complexity of transmission networks.…”

Section: Introductionmentioning

confidence: 99%

“…Kumar et al proposed a Cumulative Differential Sum procedure using K-mean clustering and weighted K-Nearest Neighbor regression [11]; as stated, this algorithm is affected by the noise and fault resistance. Singh and Sharma employed Empirical Mode Decomposition, and their algorithm overcomes the fault location impact [12], but as mentioned, it is still limited to the threshold setting which varies from network to another as in [13]. Meanwhile, Saber introduced backup protection using a phasor measuring unit (PMU) [9], despite having applied their algorithm on different scenarios but still not covering all possible cases.…”

Section: Introductionmentioning

confidence: 99%

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Fault Detection on Power Transmission Line Based on Wavelet Transform and Scalogram Image Analysis

Altaie,

Majeed,

Abderrahim

et al. 2023

Energies

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Given the massive increase in demand for electrical energy, particularly owing to global climate change and population expansion, as well as the development of complicated electrical systems due to the urgent need for a sophisticated component to enhance power delivery, it becomes important to adopt a smart and contemporary approach that is also appropriate for the aim of protecting transmission lines (TLs) and ensuring the continuous delivery of electric power to customers. Consequently, a unique and highly reliable approach for identifying faults in TLs is presented in this work, which employs Wavelet Transform and is evaluated using Matlab simulation. Wavelets of various kinds were utilized to demonstrate their dependability. Furthermore, utilizing this approach has shown itself to be highly successful and has yielded spectacular results even when it is used on a complicated electrical network. Moreover, many types of faults were presented and afterward evaluated and verified for the network in various settings, which also demonstrated their potential to recognize faults within a relatively short space of time. This innovation will alter the idea of fault detection by providing a complete and integrated model for detecting faults in a TL, and it may be regarded as a revolution in the renewal of core principles in TL protection.

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