Dr. SENOUSSAOUI Mohammed El Amine سنوساوي محمد الأمين
Faculté des Sciences et de la Technologie
Département Electrotéchnique
Grade : Maitre de conférence classe B
Numéro de Téléphone :0798653735
Adresse électronique institutionnel
Adresse électronique personnel
Adresse postale :
Rue moheiddine Ibn Mostapha Arrachidi Mamounia Mascara
Lien Google Scholar : El Amine
Lien Researchgate: El Amine


  • Optimization Of A Photovoltaic Generator With A New Solar Tracker
  • La revue : Journal of Electrical Engineering
    Domaine : Electrotechnique
    Mots Clés : Photovoltaic generator, Solar panels; tracker; yield, optimization
    Auteur : Brahami Imen Souhila, Brahami Mostefa, Tilmatine Amar, Senoussaoui Mohammed El Amine
    Issn : 1582-4594 Eissn : vol : 17, Num : 3, pp : 178-187
  • Date de publication : 2017-01-01
  • Résume :
    Electricity generation using conventional sources such as fossil fuels raises a lot of problems either economically since it is a non-renewable or environmentally, due to the release of greenhouse gas that are the main causes of global warming. Electrical energy from photovoltaic effect provides an alternative to that produced in fossil energy power plants since it is sustainable and environmentally friendly. Many kinds of research have been conducted and still developing in order to improve overall solar system’s efficiency and especially photovoltaic ones. The solar tracker is one of the elements that contribute to increase the maximum power captured from solar panels by adjusting their position in an optimal direction towards the sun. In this paper, the design of low cost, solar generator provided with a new single axis tracker (east-west) for a predetermined inclination of 30 degrees was developed. This new tracker, on sunny days, can increase the generation of the power station by 30% in comparison with a fixed system, even if it is made of cheap material, such as resistors and transistors. The generator follows the sun using a low power motor connected to a force multiplier; which provides a low energy consumption.

  • Condition monitoring of in-service oil-filled transformers: Case studies and experience
  • La revue : IEEE Electrical Insulation Magazine
    Domaine : Electrotecehniqu
    Mots Clés : Reactors , Aging , Power transformer insulation , Oil insulation , Power transformers
    Auteur : U. Mohan Rao ; I. Fofana ; A. Betie ; M. A. Senoussaoui ; M. Brahami ; E. Briosso
    Issn : 0883-7554 Eissn : 1558-4402 vol : 35, Num : 6, pp : 33 - 42
  • Date de publication : 2019-10-21
  • Résume :
    Transformers are one of the most strategic components in balancing the voltage levels and hence a high priority is given to their performance [1]. It is established that, insulation technology plays a critical role in judging the performance and service life in oil filled apparatus [2]. Performance of the insulation system depends mainly on the deterioration behavior of insulation oil and paper. The mechanisms that are responsible for premature aging of oil/paper insulation are almost the same in all the oil filled apparatus. Yet, there will be a significant difference in the intensity of the aging mechanisms in different apparatus. This intensity is attributable to rating, design, and duration of operation for different machines. The detailed discussions on these mechanisms are presented in the subsequent sections of this paper. However, aging of service insulants is unavoidable and is to be maintained at a lower rate or arrested to the greatest possible extent, such that, catastrophic failures and unscheduled outages may be mitigated [3]. Normally, utilities follow scheduled condition monitoring activities to avoid the consequences of premature aging. Hence, knowledge on these in-service condition monitoring activities will be helpful in understanding the exact deterioration rate of the insulation system. Real time in-service experience of several transformer fleets that belong to United Kingdom utilities are reported in [4]. An early degradation of insulation is noticed through increase in acidity and furan concentration in oil for several transformers in the fleet. Authors investigated this early degradation in different perspectives including loading conditions, manufacturers, and oil chemistry changes. It is inferred that changes in oil chemistry is an important attribute for early degradation and hence utilities are advised to adopt different asset management strategies for affected and unaffected transformers in a fleet. Recently, failure rate data of servic...

  • Combining and comparing different machine learning algorithms to improve dissolved gas analysis interpretation
  • La revue : IET Generation, Transmission & Distribution
    Domaine : Electrotechnique
    Mots Clés : DGA, Power transformer, Diagnosis, Machine learning, Ensemble technique
    Auteur : Mohammed El Amine Senoussaoui , Mostefa Brahami, Issouf Fofana
    Issn : 1751-8687 Eissn : 1751-8695 vol : 12, Num : 15, pp : 3673-3679
  • Date de publication : 2018-05-24
  • Résume :
    Since the discovery of dissolved gas analysis (DGA), it is considered as a leading technique for the diagnosis of liquid insulated power equipment. However, accurate analysis results can only be achieved if the measured gases closely reflect the actual equipment condition to enable an appropriate interpretation of these gases. In general, conventional techniques such as the ratio method, key gases, and Duval triangle combined or not with artificial intelligence techniques such as machine-learning algorithms are used for DGA interpretation. Here, four well-known machine-learning algorithms are compared in terms of DGA fault classification – Bayes network, multilayer perceptron, k-nearest neighbour, and J48 decision tree. Moreover, the effect of applying ensemble methods such as boosting through the Adaboost algorithm and bootstrap aggregation (bagging) is analysed, and the performances of these algorithms are evaluated. The data for developing classification models was transformed into three forms, other than the raw data. The obtained results clearly presented the efficiency and stability of some algorithms such as the J48 tree and Bayes networks for DGA fault classification, in particular, when the data is appropriately pre-processed. Moreover, the performance of these algorithms was found to consistently improve by integrating the concepts of multiple models or ensemble methods.

  • Communications

  • Lieu de communication : Algiers- Algeria
  • date debut : 2015-03-24
  • date fin : 2015-03-24
  • Lieu de communication : Maroc
  • date debut : 2013-11-13
  • date fin : 2013-11-13
  • Lieu de communication : Tunisia
  • date debut : 2013-10-04
  • date fin : 2013-10-04
  • Lieu de communication : Alger-Algérie
  • date debut : 2012-05-07
  • date fin : 2012-05-07

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