ORCID Profile
0000-0002-9765-1672
Current Organisation
University of Malaya
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Publisher: Institution of Engineering and Technology (IET)
Date: 12-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Hindawi Limited
Date: 24-08-2020
Publisher: Hindawi Limited
Date: 19-07-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2023
Publisher: Wiley
Date: 06-09-2021
DOI: 10.1002/CTA.3130
Abstract: This paper presents the implementation of selective harmonic elimination ( SHE ) in a five‐level inverter structure using artificial neural networks ( ANNs ). SHE is an effective low‐frequency modulation technique to eliminate selected harmonics and control multilevel converters. The use of ANN‐SHE requires the calculation of the optimum values of switching angles via the solving system of nonlinear equations for the total harmonic distortion ( THD ) reduction, where the nonlinear equations are founded by the complex Fourier series analysis of the inverter output voltage. The procured switching angle values are directly implemented by a multilayer perceptron ( MLP ) algorithm without a lookup table. The ANN model is obtained by training the neural network ( NN ), taking the modulation index ( M ) as an input and approximating switching angles as an output. A thorough analysis was carried out to show the programming steps of the proposed ANN‐based SHE using Matlab/Simulink environment. A realized inverter prototype steered by the proposed ANN‐based SHE was tested with various modulation indexes on a real‐time mode using a digital signal processor ( DSP ) C2000 Delfino–TMS320F28379D‐embedded board. A comparison between the simulation results and the experimental data is presented. The obtained results illustrate that the experimental results match the simulation closely, and the ANN model provides a fast and precise estimate of the switching angles for each value of the modulation index.
Publisher: MDPI AG
Date: 21-10-2022
DOI: 10.3390/EN15207811
Abstract: In the present paper, a current sensorless (CSL) method for buck-boost converter control is proposed for maximum power point tracking (MPPT) photovoltaic applications. The proposed control scheme uses the mathematical model of the buck-boost converter to derive a predefined objective function for the MPPT control. The proposed scheme does not require any current sensor and relies only on the input voltage signal, which decreases the implementation cost. The proposed method is successfully implemented using a Matlab/Simulink/Stateflow environment, and its effectiveness is compared over the perturb and observe (P& O) method. An experimental rig, that includes a buck-boost converter, a PV simulator, and a resistive load, is used for the experimental validation. A rapid Arduino prototyping platform is used for the digital implementation, where the SAM3X8E microcontroller of the Arduino DUE board, which integrates an ARM Cortex-M3 MCU, is used as a target hardware for the proposed model-based controller developed under the Stateflow environment. Furthermore, the integrated pulse width modulation (PWM) macrocell is used to generate accurate PWM gate-drive signals for the buck-boost converter. Compared to the P& O, the presented simulation and experimental results show that the proposed method has reduced the computation burden and the sensor cost of implementation by 24.3%, and 27.95%, respectively.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2021
Publisher: Elsevier BV
Date: 2023
Publisher: Elsevier BV
Date: 09-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2021
Publisher: Institute of Advanced Engineering and Science
Date: 02-2023
DOI: 10.11591/IJECE.V13I1.PP69-84
Abstract: span lang="EN-US" The operating conditions of partially shaded photovoltaic (PV) generators created a need to develop highly efficient global maximum power point tracking (GMPPT) methods to increase the PV system performance. This paper proposes a simple, efficient, and fast GMPPT based on fuzzy logic control to reach the point of global maximum power. The approach measures the PV generator current in the areas where it is almost constant to estimate the local maximums powers and extracts the highest among them. The performance of this method is evaluated firstly by simulation versus four well-known recent methods, namely the hybrid particle swarm optimization, modified cuckoo search, scrutinization fast algorithm, and shade-tolerant maximum power point tracking (MPPT) based on current-mode control. Then, experimental verification is conducted to verify the simulation findings. The results confirm that the proposed method exhibits high performance for complex partial irradiances and can be implemented in br / low-cost calculators. /span
Publisher: MDPI AG
Date: 17-03-2023
DOI: 10.3390/MATH11061457
Abstract: Tracking the speed and current in permanent magnet synchronous motors (PMSMs) for industrial applications is challenging due to various external and internal disturbances such as parameter variations, unmodelled dynamics, and external load disturbances. Inaccurate tracking of speed and current results in severe system deterioration and overheating. Therefore, the design of the controller for a PMSM is essential to ensure the system can operate efficiently under conditions of parametric uncertainties and significant variations. The present work proposes a PMSM speed controller using machine learning (ML) techniques for quick response and insensitivity to parameter changes and disturbances. The proposed ML controller is designed by learning fractional-order sliding mode control (FOSMC) controller behavior. The primary purpose of using ML in FOSMC is to avoid the self-tuning of the parameters and ensure the speed reaches the reference value in finite time with faster convergence and better tracking precision. Furthermore, the ML model does not require the mathematical model of the speed controller. In this work, several ML models are empirically evaluated on their estimation accuracy for speed tracking, namely ordinary least squares, passive-aggressive regression, random forest, and support vector machine. Finally, the proposed controller is implemented on a real-time hardware-in-the-loop (HIL) simulation platform from PLECS Inc. Comparative simulation and experimental results are presented and discussed. It is shown from the comparative study that the proposed FOSMC based on ML outperformed the traditional sliding mode control (SMC), which is more commonly used in industry in terms of tracking speed and accuracy.
Location: Algeria
Location: Algeria
No related grants have been discovered for Mostefa Kermadi.