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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Applied Power Engineering (IJAPE) Indonesian Journal of Electrical Engineering and Computer Science
El Magri, Abdelmounime
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Comprehensive review and analysis of photovoltaic energy conversion topologies Mansouri, Adil; El Magri, Abdelmounime; Younes, El Khlifi; Lajouad, Rachid; Adouairi, Mohamed Said
International Journal of Applied Power Engineering (IJAPE) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v13.i2.pp499-507

Abstract

Energy conversion is a pivotal process with widespread applications, spanning renewable energy systems, electric vehicles, and industrial power grids. Selecting the right energy conversion topology is critical for optimizing system performance, efficiency, and reliability. This comprehensive review paper provides a thorough overview of energy conversion topologies used in photovoltaic (PV) panel systems, as well as their applicability in diverse domains. Furthermore, the paper conducts a detailed analysis of commonly employed energy conversion topologies. Each topology is meticulously examined based on its operating principles, advantages, drawbacks, and typical use cases. This comprehensive review serves as an invaluable resource for researchers, engineers, and practitioners engaged in the dynamic field of energy conversion, offering insights into both wind energy and photovoltaic panel systems.
Sampled-data observer design for sensorless control of wind energy conversion system with PMSG Zaggaf, Mohammed Hicham; Mansouri, Adil; El Magri, Abdelmounime; Watil, Aziz; Lajouad, Rachid; Bahatti, Lhoussain
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp52-61

Abstract

This paper presents a nonlinear observer for a variable-speed wind energy conversion system (WECS) utilizing a permanent magnet synchronous generator (PMSG). The study addresses the design of high-gain sampled-data observers based on the nonlinear WECS model, supported by formal convergence analysis. An essential aspect of this observer design is the incorporation of a time-varying gain, significantly enhancing system performance. Convergence of estimation errors is demonstrated using the input-to-state stability method. Simulation of the proposed observer is conducted using the MATLAB-Simulink tool. The obtained results are presented and analyzed to showcase the overall effectiveness of the proposed system.
Hypovigilance detection based on analysis and binary classification of brain signals El Hadiri, Abdeljalil; Bahatti, Lhoussain; El Magri, Abdelmounime; Lajouad, Rachid
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp984-991

Abstract

Road safety has now become a priority for drivers and citizens alike, given its considerable impact on the economy and human life, which is reflected in the increase in the number of accidents worldwide. This increase is linked to a number of factors, drowsiness being one of the main causes that can lead to tragic consequences. Various systems have been developed to monitor the state of alertness. The main idea adopted in this paper is based on the integration of a biosensor to acquire the cerebral signal, then the processing and analysis of the characteristics required to detect the two states of the driver using intelligent machine learning algorithms. Two models were chosen to carry out this binary classification: The K-nearest neighbour (KNN) and logistic regression (LR) classifiers. The experimental simulation results show that the first model outperforms the second in terms of accuracy, with a percentage of 97.83% for k=3. This could lead to the development of a new safety machine brain system based on classification to control vehicle speed deceleration or activate self-driving mode in the event of hypovigilance.
Co-Authors Adouairi, Mohamed Said Bahatti, Lhoussain El Hadiri, Abdeljalil Lajouad, Rachid Mansouri, Adil Watil, Aziz Younes, El Khlifi Zaggaf, Mohammed Hicham