Abdelhamid Rabbi

Bio: Abdelhamid Rabbi is an academic researcher from University of Picardie Jules Verne. The author has contributed to research in topics: Hill climbing & Maximum power point tracking. The author has an hindex of 1, co-authored 1 publications receiving 14 citations.

The best MPPT algorithms by VSAS approach for Renewable Energy Sources (RES)

Abstract: VSAS (Variable Structure Automatic Systems) control methodology is applied to clarify the rationale behind Maximum Power Point Tracking and get the best optimization algorithm. Two algorithms are developed the Modified and Enhanced Perturb and Observe Algorithm (MEPO) the Robust Unified Control Algorithm (RUCA). The maximum power is computed online using a very simple algorithm. Compared to the other algorithms like Perturb and Observe (PO), Hill Climbing, Incremental Encoder (InCod) and and SMC approach it is proven more efficient and faster despite using low frequency commutation. The proposed MPPT has several advantages: simplicity, high convergence speed and is independent on PV array characteristics. The obtained results have proven that the MPPT is tracked even under sudden change of irradiation level.

Performance Analysis of MPPT Techniques for Dynamic Irradiation Condition of Solar PV

Abstract: Solar Photovoltaic (PV) systems are playing a major role in the present electrical energy systems. The solar PV gives nonlinear I–V and P–V characteristics. As a result, it is difficult to extract the maximum power of the solar PV. Under Partial Shading Conditions (PSCs), the solar PV characteristics consist of multiple local Maximum Power Points (MPPs) and one global MPP. The classical Maximum Power Point Tracking (MPPT) techniques cannot track the global MPP under PSCs. Accordingly, this work aims to study the performance of five soft computing MPPT techniques. The studied five soft computing MPPT techniques are Modified Variable Step Size-Radial Basis Functional Network (MVSS-RBFN), Modified Hill-Climb with Fuzzy Logic Controller (MHC-FLC), Artificial Neuro-Fuzzy Inference System (ANFIS), Perturb and Observe with Practical Swarm Optimization (P&O-PSO), and Adaptive Cuckoo Search (ACS). The comparative performance analysis of five soft computing techniques has been carried out against the Variable Step Size-Incremental Resistance (VSS-INR), and Variable Step Size-Feedback Controller (VSS-FC)-based MPPT techniques. The performance analysis of seven MPPT techniques has been done by considering the parameters are steady-state settling time, MPP tracking speed, algorithm complexity, PV array dependency, handling of partial shading, and efficiency.

Real-Time Thermoelectrical Model of PV Panels for Degradation Assessment

Abstract: In this paper, we develop a new real-time thermoelectrical model of a photovoltaic (PV) panel that takes degradations into consideration. The degradation modes under study are the potential-induced degradation, the light-induced degradation, the ultraviolet light degradation, the moisture-induced degradation, and the cell cracks. The effect of each degradation mode on the equivalent circuit of PV cells is described by equations as a function of environmental conditions and time. The thermal behavior of the PV panels is also addressed by building a thermal model of PV panels taking into consideration the environmental and operational conditions as well as the internal heat sources. At the end, we present a complete thermoelectrical model that treats the degradations as additional events. To assess the degradation impact on the cells, the normalized efficiency is taken as the key performance index.

The VSAS approach gives the Best MPPT for Solar Energy Sources

Abstract: More and more MPPT (Maximum Power Point Tracking) algorithms are in competition to maximize energy extracted from PV systems. This paper shows how to get the best algorithm (the most simple, fast and robust). VSAS (Variable Structure Automatic Systems) control methodology is applied to develop the control algorithm, to clarify the rationale behind and get the best optimization algorithm.

Modélisation et Analyse du comportement d'un Bâtiment équipé d'un Système Multi Sources d'énergie

Abstract: La problematique est d'etudier la gestion d'un systeme multi-energies, en fonction de la demande, dedie au bâtiment en milieu urbain. L'energie peut etre fournie par des panneaux photovolta iques, une eolienne, des capteurs thermiques et peut etre stockee dans des batteries et une pompe a chaleur. L'etude de ces systemes complexes necessite une bonne strategie de controle, de commande et la mise au point de methodes de gestion ecace. Le travail consiste a realiser un modele able et une simulation (realiste en temps reel) du syst eme multi-sources et d'un bâtiment de trois pieces, avec dierentes charges domestiques, comme une machine a laver, un congelateur, un refrigerateur et des systemes de chauage. Un systeme de gestion des sources d'energie qui permet d'ajuster et d'optimiser la consommation d'une facon adaptee aux ressources energetiques disponibles tout en satisfaisant la demande et maximisant le confort des occupants dans le bâtiment est ensuite etudie. Ainsi, la denition d'outils d'analyse permettra d'etudier le probleme de la gestion de l'energie pour reduire la consommation d'energie primaire et baisser la production de gaz a eet de serre tout en couvrant les besoins energetiques et en repondant a la demande. Apres un etat de l'art, le besoin en energie d'un bâtiment et ses charges, en temps reel, est etudie pour developper un modele de simulation (chapitre 2). Ensuite, dans le chapitre 3, le bâtiment est couple a un systeme multi-sources pour la production de l'energie necessaire a la demande. L'avant dernier chapitre est dedie a l'amelioration de la poursuite du point de production maximale d'energie qui peut etre fournie par le generateur PV, en exploitant des techniques d'observation et de commande pour Systemes Automatises a Structure Variable (SASV). La production d'energie est couplee, en plus des charges d'utilisation domestique, a un stockage sur batterie. Ces memes techniques SASV sont ensuite exploitees, dans le dernier chapitre, pour l'observation, l'estimation et la prediction des reserves et la gestion de l'energie stockees sur batterie.