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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) International Journal of Reconfigurable and Embedded Systems (IJRES)
Ahmed Abbou
Mohammed V University in Rabat
Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering

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Control of grid side converter in wind power based PMSG with PLL method Rania Moutchou; Ahmed Abbou
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 12, No 4: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v12.i4.pp2191-2200

Abstract

Wind power is one of the most promising renewable energy sources. Due to a constantly increasing penetration rate in power grids in order to comply with interconnection requirements. This article targets the impact of a permanent magnet synchronous generator (PMSG) which is the subject of most attention due to low cost and maintenance requirements, driven by a wind turbine with the necessary power electronic converters that allow wind turbines to operate at variable speed, and connected to the grid for power generation more efficiently by the phase-locked loop (PLL) method in order to synchronize it. Thus, the proposed control technicals are based on vector control (VC) to achieve maximum power point tracking (MPPT), keep the DC link voltage constant, and control the speed and current at the generator side and grid side in PMSG which provides controllability of the reactive power supplied to the network. Therefore, the response of the PLL is analyzed and the simulation results of the dynamic model of the system is developed in Matlab / Simulink. The study results exhibit the excellent performance with high robustness, by improving the system efficiency to 98.72%.
Online method for identifying Thevenin model parameters of Li-ion batteries and estimating SOC using EKF Mouhssine Lagraoui; Ali Nejmi; Mouna Lhayani; Mohamed Benfars; Ahmed Abbou
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v15.i1.pp54-67

Abstract

Accurate state of charge (SOC) estimation is critical for the reliable operation of battery management systems (BMS) in electric vehicles (EVs) and energy storage applications. This paper presents a method for online identification of Thevenin model (TM) parameters and SOC estimation using the extended Kalman filter (EKF). The objective is to improve estimation accuracy by precisely characterizing the SOC-dependent variations of model parameters, including open-circuit voltage (VOCV), internal resistance R1, polarization resistance R2, and capacitance C2. These parameters are identified using least squares regression based on experimental discharge data from a 1.83 Ah lithium-ion (Li-ion) battery. The resulting model is validated under pulsed discharge conditions, achieving a mean absolute error (MAE) of 0.0059 V and root mean square error (RMSE) of 0.0074 V, indicating high modeling accuracy. Subsequently, an EKF algorithm is implemented using the identified model to estimate SOC in real time. Experimental results show excellent performance with an SOC estimation MAE of 0.059% and RMSE of 0.0798%, demonstrating high precision, fast convergence, and stability. The method effectively combines empirical parameter identification with a recursive filtering technique, offering a practical and embeddable solution for BMS applications. The study concludes that accurate parameter modeling significantly enhances EKF-based SOC estimation, providing a robust foundation for real-time battery monitoring and control. 
Co-Authors Ali Nejmi Mohamed Benfars Mouhssine Lagraoui Mouna Lhayani Rania Moutchou