Mohamed Nejib Mansouri

PSO and GA-based maximum power point tracking for partially shaded photovoltaic systems

Under partial shading (PS) conditions, photovoltaic (PV) systems are popularly known to suffer from low-energy efficiency. Therefore, an effective MPPT algorithm should be used to detect the unique global peak as the maximum power point (MPP), and avoid any local maxima in order to mitigate the effect of PS. To date, various MPPT techniques have been developed to reliably track the MPP under all circumstances and reduce the energy losses due to PS. Usually, conventional methods such as Perturb and Observe (P&O) and the Incremental Conductance (IncCond), fail to extract the global MPP of the PV panel if the PV generator is partially shaded. To overcome this problem, Evolutionary Algorithms (AEs), namely the Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are studied, simulated and compared under the same software.

A 3D–2D face recognition method based on extended Gabor wavelet combining curvature and edge detection

The main limitation in 3D face recognition (FR) systems is their susceptibility to scanning difficulties and uncontrolled environments such as pose, illumination and expression variety. This paper proposes a new FR framework based on 3D to 2D mesh deforming and combined Gabor curvature and edge maps. The advantage of this method comes from the powerful saliency distribution achieved from applying extended Gabor wavelets to 2D projected face meshes. The extracted feature vectors are classified using the outstanding robustness of the support vector machine. Experiments carried out on common databases proved that valid accuracy rates can be accomplished by the proposed approach comparing to other existing methods.

A Comparative Study on Maximum Power Point Tracking Techniques of Photovoltaic Systems

This article concerns maximizing the energy reproduced from the photovoltaic PV system, ensured by using an efficient Maximum Power Point Tracking MPPT process. The process should be fast, rigorous and simple for implementation because the PV characteristics are extremely affected by fast changing conditions and Partial Shading PS. PV systems are popularly known to have many peaks one Global Peak GP and several local peaks. Therefore, the MPPT algorithm should be able to accurately detect the unique GP as the maximum power point MPP, and avoid any other peak to mitigate the effect of PS. Usually, with no shading, nearly all the conventional methods can easily reach the MPP with high efficiency. Nonetheless, they fail to extract the GP when PS occurs. To overcome this problem, Evolutionary Algorithms AEs, namely the Particle Swarm Optimization PSO and Genetic Algorithm GA are simulated and compared to the conventional methods Perturb & Observe under the same software.

Nonlinear Robust Backstepping Control for Three-Phase Grid-Connected PV Systems

This paper proposes a cascade control structure for three-phase grid-connected Photovoltaic (PV) systems. The PV system consists of a PV Generator, DC/DC converter, a DC link, a DC/AC fully controlled inverter, and the main grid. For the control process, a new control strategy using nonlinear Backstepping technique is developed. This strategy comprises three targets, namely, DC/DC converter control; tight control of the DC link voltage; and delivering the desired output power to the active grid with unity power factor (PF). Moreover, the control process relies mainly on the formulation of stability based on Lyapunov functions. Maximizing the energy reproduced from a solar power generation system is investigated as well by using the Perturb and Observe (P&O) algorithm. The Energetic Macroscopic Representation (EMR) and its reverse Maximum Control Structure (MCS) are used to provide, respectively, an instantaneous average model and a cascade control structure. The robust proposed control strategy adapts well to the cascade control technique. Simulations have been conducted using Matlab/Simulink software in order to illustrate the validity and robustness of the proposed technique under different operating conditions, namely, abrupt changing weather condition, sudden parametric variations, and voltage dips, and when facing measurement uncertainties. The problem of controlling the grid-connected PV system is addressed and dealt by using the nonlinear Backstepping control.

Hardware Implementation of a Fuzzy Logic Controller for a Hybrid Wind-Solar System in an Isolated Site

In this paper, two main contributions are presented to manage the power flow between a wind turbine and a solar power system. The first one is to use the fuzzy logic controller as an objective to find the maximum power point tracking, applied to a hybrid wind-solar system, at fixed atmospheric conditions. The second one is to respond to real-time control system constraints and to improve the generating system performance. For this, a hardware implementation of the proposed algorithm is performed using the Xilinx System Generator. The experimental simulation results show that the suggested system presents high accuracy and acceptable execution time performances. The proposed model and its control strategy offer a proper tool for optimizing the hybrid power system performance which we can use in smart house applications.

A novel optimised face recognition application based on modified shuffled frog leaping algorithm

In this work, we bring to light a novel face recognition (FR) system based on modified shuffled frog leaping algorithm (MSFLA) blended to Gabor wavelets. This new approach operates straightly on feature extraction and selection stages by providing the most propitious Gabor representations of a face image. While many researchers are seeking to find better parameterisation for Gabor filters, we introduce our evolutionary MSFLA-Gabor prototype combined to support vector machine (SVM) classifier as a robust contribution in the face biometric field. Primarily, we start by highlighting the impressive quality insured by Gabor filters in salient point extraction. Next, we present the potential dynamism of metaheuristic MSFLA in enhancing feature selection as well as up grading SVM classifier performance. Then, our optimised MSFLA-Gabor-SVM algorithm is tested on three databases under varied facial expressions, illuminations and poses. The experimental results have shown higher recognition rates and lower computational complexity scores than previous techniques.

Gabor-SVM applied to 3D-2D deformed mesh model

We propose a robust method for 3D face recognition using 3D to 2D modeling and facial curvatures detection. The 3D-2D algorithm permits to transform 3D images into 3D triangular mesh, then the mesh model is deformed and fitted to the 2D space in order to obtain a 2D smoother mesh. Then, we apply Gabor wavelets to the deformed model in order to exploit surface curves in the detection of salient face features. The classification of the final Gabor facial model is performed using the support vector machines (SVM). To demonstrate the quality of our technique, we give some experiments using the 3D AJMAL faces database. The experimental results prove that the proposed method is able to give a good recognition quality and a high accuracy rate.

Partial Transmit Sequence technique based on Particle Swarm Optimization for WOFDM PAPR reduction

This paper proposes Partial Transmit Sequence (PTS) technique for Wavelet Orthogonal Frequency Division Multiplexing (WOFDM) based on Particle Swarm Optimization (PSO) for optimum phase rotation factors searching and high Peak to Average Power Ratio (PAPR) reduction. PAPR performances of wavelet based OFDM adopting several wavelet families is compared to the conventional OFDM system. The system evaluation shows that the proposed PTS-WOFDM technique based PSO enhances the PAPR performances of conventional OFDM, PTS-WOFDM, PTS-OFDM and PSO based PTS-OFDM systems.