Power

Permanent URI for this collectionhttps://dspace.univ-boumerdes.dz/handle/123456789/3079

Browse

Author: search.filters.author.Kheldoun, Aissa (Supervisor)Subject: search.filters.subject.Optimization

Search Results

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    Identification and control of asynchronous motor using meta-heuristic algorithms
    (2023) Ghernaout, Rayane; Kheldoun, Aissa (Supervisor); Belmadani, Hamza
    The present study is centered on the examination, regulation, and enhancement of induction motors (IMs) through the application of meta-heuristic algorithms. The aim of this study is to optimize the performance and efficiency of induction motors (IMs) in various applications. The study begins with the formulation of a mathe- matical model for induction motors (IMs). Subsequently, meta-heuristic algorithms, namely EO, RSBA, and JAYA, are employed to determine the parameters of the IM.The estimation of parameters is conducted by utilizing the inputs of measured stator voltages, currents, and rotor speed. This study focuses on the modeling of indirect rotor flux-oriented control(IRFOC )and the utilization of the resulting IM param- eters to identify the motor. Control gains are then optimized through the imple- mentation of RSBA and JAYA algorithms. The findings of the simulation indicate that the system’s performance has been enhanced in comparison to conventional manual tuning techniques. The project acknowledges the difficulties involved in the optimization process and emphasizes the significance of meticulous parameterse- lection. In summary, this study serves as a valuable contribution to the progression of IM technology, highlighting its potential to enhance performance and efficiency in industrial settings.
  • No Thumbnail Available
    Item
    Deep learrning based PV power forecasters in python for different time horizons
    (2021) Chakhchoukh, Taha Yassine; Tebbal, Said; Kheldoun, Aissa (Supervisor)
    The major points worked on throughout this report are: achieving accurate fore- caster with less complexity and computational cost, using the minimum available data set for training and reaching the farthest possible span in the future. For the aim of developing forecasters in this work, then RNNs and DL were employed with the use of the python programming language for their modelling. A data set of GHI recordings collected during January 21, 2011, through March 4, 2012 and from December 20, 2012, through January 20, 2014 is used to compare the above DNN based models for three different time spans. Moreover, various evaluation metrics such as MAPE, RMSE, r and R2 have been used for the assessment of the models to explore their performance when spanning different time horizons such that each one has a specific training samples. The obtained results have showed that the AE LSTM is the most efficient and less sensitive to the number of training samples.