A real-time fault diagnosis system for high-speed power system protection based on machine learning algorithms

Khoudry, Elmahdi; Belfqih, Abdelaziz; Ouaderhman, Tayeb; Boukherouaa, Jamal; Elmariami, Faissal

doi:10.11591/ijece.v10i6.pp6122-6138

Open access Jurnal SINTA 1

A real-time fault diagnosis system for high-speed power system protection based on machine learning algorithms

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 10 No. 6 (2020)

DOI: 10.11591/ijece.v10i6.pp6122-6138
ISSN: 2722-2578, 2088-8708
Published: 2020-12-01
Publisher: Institute of Advanced Engineering and Science
License: creativecommons.org/licenses/by-sa/4.0

Abstract

This paper puts forward a real-time smart fault diagnosis system (SFDS) intended for high-speed protection of power system transmission lines. This system is based on advanced signal processing techniques, traveling wave theory results, and machine learning algorithms. The simulation results show that the SFDS can provide an accurate internal/external fault discrimination, fault inception time estimation, fault type identification, and fault location. This paper presents also the hardware requirements and software implementation of the SFDS.

Collaboration network of the authors

16 authors · 47 co-authorship links · 4 across different organisations

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University of Hassan II Casablanca
Cadi Ayyad University
Affiliation unknown

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Same organisation
Affiliation unknown
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Co-author	Organisation	Joint articles
Mohamed Nouh Dazahra	Affiliation unknown	12
Faissal El Mariami	University of Hassan II Casablanca	11
Nazha Cherkaoui	Affiliation unknown	6
Anass Lekbich	University of Hassan II Casablanca	6
Abdelmajid Berdai	University of Hassan II Casablanca	4
Chaimae Zedak	University of Hassan II Casablanca	3
Ali Tarraq	University of Hassan II Casablanca	2
Touria Haidi	University of Hassan II Casablanca	2
Naima Agouzoul	University of Hassan II Casablanca	2
Aziz Oukennou	Cadi Ayyad University	2
Rabiaa Gadal	University of Hassan II Casablanca	2

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.

Metadata as oai_dc, converted from Crossref

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