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All Journal International Journal of Electrical and Computer Engineering International Journal of Power Electronics and Drive Systems (IJPEDS)
Tijani Lamhamdi
Sidi Mohamed Ben Abdellah University
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Intelligent control of battery energy storage for microgrid energy management using ANN Younes Boujoudar; Mohamed Azeroual; Hassan Elmoussaoui; Tijani Lamhamdi
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i4.pp2760-2767

Abstract

In this paper, an intelligent control strategy for a microgrid system consisting of Photovoltaic panels, grid-connected, and li-ion battery energy storage systems proposed. The energy management based on the managing of battery charging and discharging by integration of a smart controller for DC/DC bidirectional converter. The main novelty of this solution are the integration of artificial neural network (ANN) for the estimation of the battery state of charge (SOC) and for the control of bidirectional converter. The simulation results obtained in the MATLAB/Simulink environment explain the performance and the robust of the proposed control technique.
Lithium-Ion batteries modeling and state of charge estimation using Artificial Neural Network Younes Boujoudar; Hassan Elmoussaoui; Tijani Lamhamdi
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 5: October 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (634.375 KB) | DOI: 10.11591/ijece.v9i5.pp3415-3422

Abstract

In This paper, we propose an effective and online technique for modeling nd State of Charge (SoC) estimation of Lithium-Ion (Li-Ion) batteries using Feed Forward Neural Networks(FFNN) and Nonlinear Auto Regressive model with eXogenous input(NARX). The both Artificial Neural Network (ANN) are rained using the data collected from the batterycharging and discharging pro ess. The NARX network finds the needed battery model, where the input ariables are the battery terminal voltage, SoC at the previous sample, and the urrent, temperature at the present sample. The proposed method is imple mented on a Li-Ion battery cell to estimate online SoC. Simulation results show good estimation of theSoC.
Performance analysis and enhancement of direct power control of DFIG based wind system Mohamed Amine Beniss; Hassan El Moussaoui; Tijani Lamhamdi; Hassane El Markhi
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 12, No 2: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v12.i2.pp1034-1044

Abstract

The paper proposes a complete modeling and control technique of variable speed wind turbine system (WTS) based on the doubly fed induction generator (DFIG). Two levels back-to-back converter is used to ensure the energy transfer between the DFIG rotor and the grid. The wind turbine to operate efficiently, a maximum power point tracking (MPPT) algorithm is implemented. Then, direct power control (DPC) strategy has been combined with the MPPT technique in order to guarantee the selection of the appropriate rotor voltage vectors and to minimize the active and reactive power errors. Finally, the simulation is performed by using MATLAB/simulink platform basing on 7.5KW DFIG wind generation system, and the results prove the effectiveness of our proposed control technique.
A deterministic method of distributed generation hosting capacity calculation: case study of underground distribution grid in Morocco soukaina naciri; Ismail Moufid; Hassan El Moussaoui; Tijani Lamhamdi; Hassane El Markhi
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp144-158

Abstract

Global warming has become a significant concern over the past decades. As a result, governments have shifted their policies toward renewable energy sources and environmentally friendly industries. This approach requires a renewal of the electrical networks to accommodate this new intermittent generation (from solar and wind sources) while remaining stable and reliable. In this vision, the notion of hosting capacity has been introduced to define the amount of new distributed generation that an electrical network can host without affecting its stability and reliability. This study proposes a deterministic method based on the π model of cables to estimate the underground feeder's hosting capacity. This method considers reverse power flow, overvoltage, reconfiguration, overloading, and the physical characteristics of lines. It is applied to the Moroccan medium voltage underground radial feeder. Through DIgSILENT power factory software, the power flow analysis is carried out to validate its effectiveness in overcoming overvoltage and cable overload problems. The results validate the relevance of our method, its reliability, its fluidity of application, and its ability to maintain performance indices within the acceptable range.
Co-Authors Hassan El Moussaoui Hassan Elmoussaoui Hassane El Markhi Ismail Moufid Mohamed Amine Beniss Mohamed Azeroual soukaina naciri Younes Boujoudar