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All Journal International Journal of Electrical and Computer Engineering International Journal of Power Electronics and Drive Systems (IJPEDS)
Abdelaziz Belfqih
Hassan II University
Computer Science & IT Control & Systems Engineering 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.
Traveling wave based fault location for power transmission lines using morphological filters and clarke modal components Elmahdi Khoudry; Abdelaziz Belfqih; Jamal Boukherouaa; Faissal Elmariami
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 2: April 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (909.581 KB) | DOI: 10.11591/ijece.v10i2.pp1122-1134

Abstract

This article presents a fast and accurate fault location approach for power transmission lines based on the theory of traveling waves. In fact, when faults occur, they give rise to transient voltages and currents that propagate at a speed close to that of light along the transmission line as traveling waves. Moreover, according to the superposition theorem, each of these transients is a combination of a steady-state quantity and an incremental quantity. These transient signals measured at both ends of the line are first transformed to the Clarke (0-α-β components) components in order to categorize the type of faults, and then multi-scale morphological gradient filters are used to extract equivalent quantities to the incremental quantities to form what are called characteristic signals. These latter will be used to identify the fault location according to the proposed algorithm.
An intelligent energy management system for optimum design and real-time operation Chaimae Zedak; Abdelaziz Belfqih; Jamal Boukherouaa; Faissal El Mariami
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 14, No 1: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v14.i1.pp480-495

Abstract

Planning and management of distribution networks has become a very difficult task, especially with the strong expansion of renewable energy sources (RES) which are intermittent in nature. Maintaining fluidity and reliability of real-time decisions while taking into consideration uncertainties related to production and increasing the profit of distribution network operators is the objective of the system proposed in this work. It is an intelligent energy management system dedicated to the management of grid-integrated RES and battery energy storage systems (BESS), composed of: i) a real-time control and data acquisition model, ii) a model for forecasting the intermittent parameters of RES based on neural networks, iii) a long-term planning model based on the optimal placement and size of RES and BESS, and iv) an hourly planning model for scheduling the energy distribution between energy sources. The non-dominated sorting genetic algorithm and the entropy-TOPSIS method (technique for order of preference by similarity to ideal solution) form the basic block of this model. To evaluate it, a modified IEEE 33 bus network was used for testing and the results, for short-term scheduling, proved that the system succeeds in maximizing profits and significantly minimizing CO2 emissions, in addition to power losses and voltage drops.
Co-Authors Chaimae Zedak Elmahdi Khoudry Elmahdi Khoudry Faissal El Mariami Faissal Elmariami Jamal Boukherouaa Tayeb Ouaderhman