Power

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

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Start date: 2020Subject: search.filters.subject.Optimization

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Now showing 1 - 5 of 5
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    Model predictive optimization based energy storage system in distributed system
    (Université M’Hamed BOUGARA de Boumerdes : Institut de génie electrique et electronique (IGEE), 2023) Riche, Aboubaker; Boutora, Saliha (Supervisor)
    The main goal of this project is to develop an MPC controller they will improve the performance of the storage system , by controlling the power flow between the battery energy storage system (BESS) and the supercapacitor (SC). To accomplish task stressing grid-forming hybrid energy storage systems and the supercapacitor's state of charge (SoC) by simulate rapid load variations and fast photovoltaic (PV) power fluctuations, also developing an MPC controller Is depends to efficient controlling power flow, restore to the SoC of SC after sudden load changes and limits its SoC variation in a predefined range, to ensure the continuous operation of SC. The performance of the proposed approach is then simulated using MATLAB/Simulink.
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    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.
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    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.
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    Smart metering system optimization using global algorithm
    (2021) Bedjil, Amine; Harir, Mustapha; Recioui, Abdelmadjid (Supervisor)
    Non-technical losses or electricity theft have been a serious problem in many developing countries for a long time. This study aims to develop a practical method for determining and reducing the non-technical losses in the power grid by detecting where the suspicion of incorrect registration of electricity consumption occurs and reveal the electricity theft. The proposed method summarizes a mathematical optimization method and modeling technique of smart metering system optimization by using a particular algorithm to identify and minimize the measurement errors for increasing the electricity readings accuracy and lowering the electricity losses and related costs.
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    Load balancing in smart grids using multi-objective evolutionary optimization technique
    (2020) Abdi, Lina Amel; Recioui, A. (Supervisor)
    The work concerns the balance between the supplied and consumed power in a residential area. An optimization task is formulated and solved. The re-formulation of the optimization task to include renewables is also provided. It was revealed in the results that with the proposed optimization technique, a significant reduction in the energy usage cost and the waiting time of the appliances (delay) for residential consumers can be achieved.This work can be used in smart home and smart cities applications.