A Novel Algorithm for Greatly Accurate Electrical Fault Detection and Classification Based on Haar Wavelet

Milton Ruiz; Manuel Jaramillo

doi:10.1007/978-3-030-99170-8_11

A Novel Algorithm for Greatly Accurate Electrical Fault Detection and Classification Based on Haar Wavelet

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

Abstract

The present investigation presents an algorithm for fault detection and classification in transmission lines using the Haar-type wavelet mother transform. Voltage and current signals contain all the information of the power system, therefore when a fault occurs in the electrical power system, these signals present disturbances in their amplitude, phase changes and presence of harmonics. Mathematically, all mother wavelets respond with an impulse when there is an abrupt change in the signals, this property allows to detect when a fault has occurred (in the time domain). Experimental results have shown that with frequencies above 100 kHz it is possible to detect a fault with 100% accuracy by taking only 4 samples of the signal and applying the Haar wavelet. The fault detection times vary between 0.31 ms and 1.15 ms and the fault classification times vary between 0.94 s and 1.31 s.

Original language	English
Title of host publication	Smart Technologies, Systems and Applications - 2nd International Conference, SmartTech-IC 2021, Revised Selected Papers
Editors	Fabián R. Narváez, Julio Proaño, Paulina Morillo, Diego Vallejo, Daniel González Montoya, Gloria M. Díaz
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	143-157
Number of pages	15
ISBN (Print)	9783030991692
DOIs	https://doi.org/10.1007/978-3-030-99170-8_11
State	Published - 2022
Event	2nd International Conference on Smart Technologies, Systems and Applications, SmartTech-IC 2021 - Quito, Ecuador Duration: 1 Dec 2021 → 3 Dec 2021

Publication series

Name	Communications in Computer and Information Science
Volume	1532 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	2nd International Conference on Smart Technologies, Systems and Applications, SmartTech-IC 2021
Country/Territory	Ecuador
City	Quito
Period	1/12/21 → 3/12/21

Bibliographical note

Publisher Copyright:
© 2022, Springer Nature Switzerland AG.

Keywords

Fault detection (FD)
Fault diagnosis
Fault location (FL)
Fault-type classification (FC)
Wavelet transforms

Access to Document

10.1007/978-3-030-99170-8_11

Cite this

Ruiz, M., & Jaramillo, M. (2022). A Novel Algorithm for Greatly Accurate Electrical Fault Detection and Classification Based on Haar Wavelet. In F. R. Narváez, J. Proaño, P. Morillo, D. Vallejo, D. González Montoya, & G. M. Díaz (Eds.), Smart Technologies, Systems and Applications - 2nd International Conference, SmartTech-IC 2021, Revised Selected Papers (pp. 143-157). (Communications in Computer and Information Science; Vol. 1532 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-99170-8_11

Ruiz, Milton ; Jaramillo, Manuel. / A Novel Algorithm for Greatly Accurate Electrical Fault Detection and Classification Based on Haar Wavelet. Smart Technologies, Systems and Applications - 2nd International Conference, SmartTech-IC 2021, Revised Selected Papers. editor / Fabián R. Narváez ; Julio Proaño ; Paulina Morillo ; Diego Vallejo ; Daniel González Montoya ; Gloria M. Díaz. Springer Science and Business Media Deutschland GmbH, 2022. pp. 143-157 (Communications in Computer and Information Science).

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abstract = "The present investigation presents an algorithm for fault detection and classification in transmission lines using the Haar-type wavelet mother transform. Voltage and current signals contain all the information of the power system, therefore when a fault occurs in the electrical power system, these signals present disturbances in their amplitude, phase changes and presence of harmonics. Mathematically, all mother wavelets respond with an impulse when there is an abrupt change in the signals, this property allows to detect when a fault has occurred (in the time domain). Experimental results have shown that with frequencies above 100 kHz it is possible to detect a fault with 100% accuracy by taking only 4 samples of the signal and applying the Haar wavelet. The fault detection times vary between 0.31 ms and 1.15 ms and the fault classification times vary between 0.94 s and 1.31 s.",

keywords = "Fault detection (FD), Fault diagnosis, Fault location (FL), Fault-type classification (FC), Wavelet transforms",

author = "Milton Ruiz and Manuel Jaramillo",

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Ruiz, M & Jaramillo, M 2022, A Novel Algorithm for Greatly Accurate Electrical Fault Detection and Classification Based on Haar Wavelet. in FR Narváez, J Proaño, P Morillo, D Vallejo, D González Montoya & GM Díaz (eds), Smart Technologies, Systems and Applications - 2nd International Conference, SmartTech-IC 2021, Revised Selected Papers. Communications in Computer and Information Science, vol. 1532 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 143-157, 2nd International Conference on Smart Technologies, Systems and Applications, SmartTech-IC 2021, Quito, Ecuador, 1/12/21. https://doi.org/10.1007/978-3-030-99170-8_11

A Novel Algorithm for Greatly Accurate Electrical Fault Detection and Classification Based on Haar Wavelet. / Ruiz, Milton ; Jaramillo, Manuel.
Smart Technologies, Systems and Applications - 2nd International Conference, SmartTech-IC 2021, Revised Selected Papers. ed. / Fabián R. Narváez; Julio Proaño; Paulina Morillo; Diego Vallejo; Daniel González Montoya; Gloria M. Díaz. Springer Science and Business Media Deutschland GmbH, 2022. p. 143-157 (Communications in Computer and Information Science; Vol. 1532 CCIS).

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

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AU - Jaramillo, Manuel

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N2 - The present investigation presents an algorithm for fault detection and classification in transmission lines using the Haar-type wavelet mother transform. Voltage and current signals contain all the information of the power system, therefore when a fault occurs in the electrical power system, these signals present disturbances in their amplitude, phase changes and presence of harmonics. Mathematically, all mother wavelets respond with an impulse when there is an abrupt change in the signals, this property allows to detect when a fault has occurred (in the time domain). Experimental results have shown that with frequencies above 100 kHz it is possible to detect a fault with 100% accuracy by taking only 4 samples of the signal and applying the Haar wavelet. The fault detection times vary between 0.31 ms and 1.15 ms and the fault classification times vary between 0.94 s and 1.31 s.

AB - The present investigation presents an algorithm for fault detection and classification in transmission lines using the Haar-type wavelet mother transform. Voltage and current signals contain all the information of the power system, therefore when a fault occurs in the electrical power system, these signals present disturbances in their amplitude, phase changes and presence of harmonics. Mathematically, all mother wavelets respond with an impulse when there is an abrupt change in the signals, this property allows to detect when a fault has occurred (in the time domain). Experimental results have shown that with frequencies above 100 kHz it is possible to detect a fault with 100% accuracy by taking only 4 samples of the signal and applying the Haar wavelet. The fault detection times vary between 0.31 ms and 1.15 ms and the fault classification times vary between 0.94 s and 1.31 s.

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Ruiz M , Jaramillo M. A Novel Algorithm for Greatly Accurate Electrical Fault Detection and Classification Based on Haar Wavelet. In Narváez FR, Proaño J, Morillo P, Vallejo D, González Montoya D, Díaz GM, editors, Smart Technologies, Systems and Applications - 2nd International Conference, SmartTech-IC 2021, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2022. p. 143-157. (Communications in Computer and Information Science). doi: 10.1007/978-3-030-99170-8_11