Detection of Fault on Transmission Line Using Wavelet Transform and Neural Network Backpropagation

Ennyvathana Em; Ardyono Priyadi; Dimas Anton Asfani

doi:10.1109/ISITIA66279.2025.11137504

Detection of Fault on Transmission Line Using Wavelet Transform and Neural Network Backpropagation

Institut Teknologi Sepuluh Nopember

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Reliability fault detection is crucial for keeping power systems stable, especially since many faults are caused by environmental or electrical failure. Although Discrete Wavelet Transform (DWT) and Backpropagation Neural Network (BPNN) have been widely explored, most studies rely on standard IEEE test systems. This study takes a different approach by applying these methods to the modified Kirirum 1 and Kirirum 3 transmission lines in Kampong Speu Province, Cambodia. Using DWT to obtain characteristics from voltage and current waveforms and BPNN for identifying fault types and localization, the model achieved 98.33% accuracy, under 10% localization error, and 0.7 seconds of detection time across 120 scenarios with fault resistance levels of 10Ω and 30Ω, showing strong potential for localized networks.

Original language	English
Title of host publication	26th International Seminar on Intelligent Technology and Its Applications
Subtitle of host publication	Fostering Equal Opportunities for Breakthrough Technology Innovations, ISITIA 2025 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	100-105
Number of pages	6
Edition	2025
ISBN (Electronic)	9798331537609
DOIs	https://doi.org/10.1109/ISITIA66279.2025.11137504
Publication status	Published - 2025
Event	26th International Seminar on Intelligent Technology and Its Applications, ISITIA 2025 - Hybrid, Surabaya, Indonesia Duration: 23 Jul 2025 → 25 Jul 2025

Conference

Conference	26th International Seminar on Intelligent Technology and Its Applications, ISITIA 2025
Country/Territory	Indonesia
City	Hybrid, Surabaya
Period	23/07/25 → 25/07/25

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

BackPropagation Neural Network (BPNN)
Discrete Wavelet Transform (DWT)
Fault Classification
Fault Detection
Fault Location
Transmission Line

Access to Document

10.1109/ISITIA66279.2025.11137504

Cite this

Em, E., Priyadi, A., & Asfani, D. A. (2025). Detection of Fault on Transmission Line Using Wavelet Transform and Neural Network Backpropagation. In 26th International Seminar on Intelligent Technology and Its Applications: Fostering Equal Opportunities for Breakthrough Technology Innovations, ISITIA 2025 - Proceedings (2025 ed., pp. 100-105). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISITIA66279.2025.11137504

Em, Ennyvathana ; Priyadi, Ardyono ; Asfani, Dimas Anton. / Detection of Fault on Transmission Line Using Wavelet Transform and Neural Network Backpropagation. 26th International Seminar on Intelligent Technology and Its Applications: Fostering Equal Opportunities for Breakthrough Technology Innovations, ISITIA 2025 - Proceedings. 2025. ed. Institute of Electrical and Electronics Engineers Inc., 2025. pp. 100-105

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title = "Detection of Fault on Transmission Line Using Wavelet Transform and Neural Network Backpropagation",

abstract = "Reliability fault detection is crucial for keeping power systems stable, especially since many faults are caused by environmental or electrical failure. Although Discrete Wavelet Transform (DWT) and Backpropagation Neural Network (BPNN) have been widely explored, most studies rely on standard IEEE test systems. This study takes a different approach by applying these methods to the modified Kirirum 1 and Kirirum 3 transmission lines in Kampong Speu Province, Cambodia. Using DWT to obtain characteristics from voltage and current waveforms and BPNN for identifying fault types and localization, the model achieved 98.33\% accuracy, under 10\% localization error, and 0.7 seconds of detection time across 120 scenarios with fault resistance levels of 10Ω and 30Ω, showing strong potential for localized networks.",

keywords = "BackPropagation Neural Network (BPNN), Discrete Wavelet Transform (DWT), Fault Classification, Fault Detection, Fault Location, Transmission Line",

author = "Ennyvathana Em and Ardyono Priyadi and Asfani, \{Dimas Anton\}",

note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 26th International Seminar on Intelligent Technology and Its Applications, ISITIA 2025 ; Conference date: 23-07-2025 Through 25-07-2025",

year = "2025",

doi = "10.1109/ISITIA66279.2025.11137504",

language = "English",

pages = "100--105",

booktitle = "26th International Seminar on Intelligent Technology and Its Applications",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Em, E, Priyadi, A & Asfani, DA 2025, Detection of Fault on Transmission Line Using Wavelet Transform and Neural Network Backpropagation. in 26th International Seminar on Intelligent Technology and Its Applications: Fostering Equal Opportunities for Breakthrough Technology Innovations, ISITIA 2025 - Proceedings. 2025 edn, Institute of Electrical and Electronics Engineers Inc., pp. 100-105, 26th International Seminar on Intelligent Technology and Its Applications, ISITIA 2025, Hybrid, Surabaya, Indonesia, 23/07/25. https://doi.org/10.1109/ISITIA66279.2025.11137504

Detection of Fault on Transmission Line Using Wavelet Transform and Neural Network Backpropagation. / Em, Ennyvathana; Priyadi, Ardyono ; Asfani, Dimas Anton.
26th International Seminar on Intelligent Technology and Its Applications: Fostering Equal Opportunities for Breakthrough Technology Innovations, ISITIA 2025 - Proceedings. 2025. ed. Institute of Electrical and Electronics Engineers Inc., 2025. p. 100-105.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Detection of Fault on Transmission Line Using Wavelet Transform and Neural Network Backpropagation

AU - Em, Ennyvathana

AU - Priyadi, Ardyono

AU - Asfani, Dimas Anton

PY - 2025

Y1 - 2025

N2 - Reliability fault detection is crucial for keeping power systems stable, especially since many faults are caused by environmental or electrical failure. Although Discrete Wavelet Transform (DWT) and Backpropagation Neural Network (BPNN) have been widely explored, most studies rely on standard IEEE test systems. This study takes a different approach by applying these methods to the modified Kirirum 1 and Kirirum 3 transmission lines in Kampong Speu Province, Cambodia. Using DWT to obtain characteristics from voltage and current waveforms and BPNN for identifying fault types and localization, the model achieved 98.33% accuracy, under 10% localization error, and 0.7 seconds of detection time across 120 scenarios with fault resistance levels of 10Ω and 30Ω, showing strong potential for localized networks.

AB - Reliability fault detection is crucial for keeping power systems stable, especially since many faults are caused by environmental or electrical failure. Although Discrete Wavelet Transform (DWT) and Backpropagation Neural Network (BPNN) have been widely explored, most studies rely on standard IEEE test systems. This study takes a different approach by applying these methods to the modified Kirirum 1 and Kirirum 3 transmission lines in Kampong Speu Province, Cambodia. Using DWT to obtain characteristics from voltage and current waveforms and BPNN for identifying fault types and localization, the model achieved 98.33% accuracy, under 10% localization error, and 0.7 seconds of detection time across 120 scenarios with fault resistance levels of 10Ω and 30Ω, showing strong potential for localized networks.

KW - BackPropagation Neural Network (BPNN)

KW - Discrete Wavelet Transform (DWT)

KW - Fault Classification

KW - Fault Detection

KW - Fault Location

KW - Transmission Line

UR - https://www.scopus.com/pages/publications/105016904092

U2 - 10.1109/ISITIA66279.2025.11137504

DO - 10.1109/ISITIA66279.2025.11137504

M3 - Conference contribution

AN - SCOPUS:105016904092

SP - 100

EP - 105

BT - 26th International Seminar on Intelligent Technology and Its Applications

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 26th International Seminar on Intelligent Technology and Its Applications, ISITIA 2025

Y2 - 23 July 2025 through 25 July 2025

ER -

Em E, Priyadi A , Asfani DA. Detection of Fault on Transmission Line Using Wavelet Transform and Neural Network Backpropagation. In 26th International Seminar on Intelligent Technology and Its Applications: Fostering Equal Opportunities for Breakthrough Technology Innovations, ISITIA 2025 - Proceedings. 2025 ed. Institute of Electrical and Electronics Engineers Inc. 2025. p. 100-105 doi: 10.1109/ISITIA66279.2025.11137504

Detection of Fault on Transmission Line Using Wavelet Transform and Neural Network Backpropagation

Abstract

Conference

UN SDGs

Keywords

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