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Tayeb Ouaderhman
Hassan II University
Computer Science & IT Electrical & Electronics Engineering

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Multi-scale morphological gradient algorithm based ultra-high-speed directional transmission line protection for internal and external fault discrimination Elmahdi Khoudry; Abdelaziz Belfqih; Tayeb Ouaderhman; Jamal Boukherouaa; Faissal Elmariami
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 5: October 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2796.917 KB) | DOI: 10.11591/ijece.v9i5.pp3891-3904

Abstract

This paper introduces an ultra-high-speed directional transmission line protection scheme based on multi-scale morphological gradient algorithm (MSMGA). The directional protection scheme sets down the rules for determining the fault position in relation to the relaying point. The MSMGA is used to extract the fault-induced transient characteristics contained in the voltage and current signals. The associated signals are formed from these transient characteristics and the polarity of their local modulus maxima allow the discrimination between internal and external faults.
A real-time fault diagnosis system for high-speed power system protection based on machine learning algorithms Elmahdi Khoudry; Abdelaziz Belfqih; Tayeb Ouaderhman; Jamal Boukherouaa; Faissal Elmariami
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v10i6.pp6122-6138

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.
Co-Authors Abdelaziz Belfqih Elmahdi Khoudry Elmahdi Khoudry Faissal Elmariami Jamal Boukherouaa