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Engineering（科研） 2015年第1期7卷 36-45页

作者： Abdelmajid Berdai Abdelaziz Belfqih Jamal Boukherouaa faissal el mariami Abdelhamid Hmidat Vladimir Vlasenko Valery Titjuk TCSI Laboratory ESE Research Group Department of Electrical Engineering National School of Electricity and Mechanics (ENSEM) University Hassan II Ain Chock Casablanca Morocco Department of electromechanics Krivoy Rog National University Krivoy Rog Ukraine

The switched reluctance motor is appreciated in variable speed drives through his simplicity of design, his low cost, his high reliability and high ability to regulate which allow him to compete with the other common types of electric drives. The mathematical modelling of switched reluctance motors (SRM) allows his study in the areas of research and conception. In this paper, we present the developing of a methodology using the combined model of switched reluctance motor drive bases in standard equations of electrical machines with the field theory analysis of magnetic circuit. Then, the elaboration of mathematical models for switching reluctance motor (SRM) proposes his application based on a similarity with series DC motor to simplify the conception of control of electrical drives systems [1] with Closed Loop Control.

关键词： Switched Reluctance Motor Similarity Equivalent Parameters

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Application of four machine-learning methods to predict short-horizon wind energy

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Global Energy Interconnection 2023年第6期6卷 726-737页

作者： Doha Bouabdallaoui Touria Haidi faissal elmariami Mounir Derri el Mehdi Mellouli Laboratory LAGES Ecole Hassania des Travaux Publics(EHTP)Casablanca 20230Morocco Laboratory LESE Ecole Nationale Supérieure d’Electricitéet de Mécanique(ENSEM)Casablanca 20100Morocco Laboratory LESA School of Applied SciencesSidi Mohamed Ben Abdellah UniversityFez 30000Morocco

Renewable energy has garnered attention due to the need for sustainable energy *** power has emerged as an alternative that has contributed to the transition towards cleaner *** the importance of wind energy grows,it can be crucial to provide forecasts that optimize its performance *** intelligence(AI)methods have risen in prominence due to how well they can handle complicated systems while enhancing the accuracy of *** study explored the area of AI to predict wind-energy production at a wind farm in Yalova,Turkey,using four different AI approaches:support vector machines(SVMs),decision trees,adaptive neuro-fuzzy inference systems(ANFIS)and artificial neural networks(ANNs).Wind speed and direction were considered as essential input parameters,with wind energy as the target parameter,and models are thoroughly evaluated using metrics such as the mean absolute percentage error(MAPE),coefficient of determination(R~2),and mean absolute error(MAE).The findings accentuate the superior performance of the SVM,which delivered the lowest MAPE(2.42%),the highest R~2(0.95),and the lowest MAE(71.21%)compared with actual values,while ANFIS was less effective in this *** main aim of this comparative analysis was to rank the models to move to the next step in improving the least efficient methods by combining them with optimization algorithms,such as metaheuristic algorithms.

关键词： Wind Energy Prediction Support Vector Machines Decision Trees Adaptive Neuro-Fuzzy Inference Systems Artificial Neural Networks

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A Novel Decoder Based on Parallel Genetic Algorithms for Linear Block Codes

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International Journal of Communications, Network and System Sciences 2013年第1期6卷 66-76页

作者： Abdeslam Ahmadi faissal el Bouanani Hussain Ben-Azza Youssef Benghabrit Department of Communication Networks National High School of Comptuer Science and System Analysis Rabat Morocco Department of Industrial and Production Engineering Moulay Ismail University National High School of Arts and Trades Meknès Morocco

Genetic algorithms offer very good performances for solving large optimization problems, especially in the domain of error-correcting codes. However, they have a major drawback related to the time complexity and memory occupation when running on a uniprocessor computer. This paper proposes a parallel decoder for linear block codes, using parallel genetic algorithms (PGA). The good performance and time complexity are confirmed by theoretical study and by simulations on BCH(63,30,14) codes over both AWGN and flat Rayleigh fading channels. The simulation results show that the coding gain between parallel and single genetic algorithm is about 0.7 dB at BER = 10﹣5 with only 4 processors.

关键词： Channel Coding Linear Block Codes Meta-Heuristics Parallel Genetic Algorithms Parallel Decoding Algorithms Time Complexity Flat Fading Channel AWGN

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