Publication:
Winding-to-ground fault location in power transformer windings using combination of discrete wavelet transform and back-propagation neural network

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Issued Date

2022

Resource Type

Article

Language

eng

File Type

application/pdf

ISSN

20452322

DOI

10.1038/s41598-022-24434-9

Rights Holder(s)

Scopus

Bibliographic Citation

Journal of Oral Science. Vol 64, No.4 (2022), p.271-273

Suggested Citation

Chiradeja P., Ngaopitakkul A. Winding-to-ground fault location in power transformer windings using combination of discrete wavelet transform and back-propagation neural network. Journal of Oral Science. Vol 64, No.4 (2022), p.271-273. doi:10.1038/s41598-022-24434-9 Retrieved from: https://hdl.handle.net/20.500.14740/10085

Title

Winding-to-ground fault location in power transformer windings using combination of discrete wavelet transform and back-propagation neural network

Author(s)

Chiradeja P.
 Ngaopitakkul A.

Abstract

Power transformers are important equipment in power systems and require a responsive and accurate protection system to ensure system reliability. In this paper, a fault location algorithm for power transformers based on the discrete wavelet transform and back-propagation neural network is presented. The system is modelled on part of Thailand’s transmission and distribution system. The ATP/EMTP software is used to simulate fault signals to validate the proposed algorithm, and the performance is evaluated under various conditions. In addition, various activation functions in the hidden and output layers are compared to select suitable functions for the algorithm. Test results show that the proposed algorithm can correctly locate faults on the transformer winding under different conditions with an average error of less than 0.1%. This result demonstrates the feasibility of implementing the proposed algorithm in actual protection systems for power transformers. © 2022, The Author(s).

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URI

https://hdl.handle.net/20.500.14740/10085

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Scopus: Year 2022

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