Browsing by Author "Medjoudj, Rafik (Supervisor)"

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    Industrial power training system
    (2021) Gasmi, Ali; Tiouche, Ridha; Medjoudj, Rafik (Supervisor)
    There has been an increasing emphasis on enhancing students' practical experience acquiring from the higher education, focusing not only on the development of academic and intellectual capabilities and subject knowledge, but also on the development of skills to equip students for employability. A practical training system that allows instruments to be monitored and controlled over LabVIEW leaves plenty of room to be studied. This training system can facilitate performing experiments in a safe environment and allows students to control and obtain real-time measurements or experimental data. In this report the system was tested on several motor starter circuits like DOL and VFD starters with a three-phase induction motor who draws a high starting current and high torque during start-up which can damage the motor and here comes the role of the starters to protect it. This report will also provide a suggestion of which starter is most suitable for an application based on the stated constraints.
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    PV power forecasting using two of the most effective techniques
    (2021) Mellouki, Charaf Eddine; Bouraoui, Rida Mohamed; Medjoudj, Rafik (Supervisor)
    Accurate forecasting of photovoltaic energy production from renewable resources is crucial for economic reasons. In this report we discuss the use of both Machine learning forecasting techniques SVM and ANNs techniques.We compare between the two methods to predict the output of the PV output power, the data used consist of samples covering different weather conditions and error evaluation indexes RMSE MAE are used to determine the most efficient technique.SVM Technique is implemented by three different equations: Linear, Quadratic and Cubic equations, the performance results shows a slight differences between the first two MAE (9.2816% 9.9556%), RMSE (12.562% 12.59%) respectively while the last model outperforms its predecessors MAE (8.7952%) RMSE (11.432 %). The second technique which is implemented by MLPs and Elman shows ever better performances and efficiency than previous models with error indexes RMSE (6.79% 4.75%), MAE (0.21720%, 0.112%), the Elman RNN is more accurate than the Multi-Layer Perceptron and sows better results on good weather conditions wile bot models sow unstable performance is much less suitable conditions. The results of this report is to identify the best forecasting technique to be used in further esearch.