Mohammed Boussetta

Clustering or grouping is among the most important image processing methods that aim to split an image into different groups. Examining the literature, many clustering algorithms have been carried out, where the K-means algorithm is considered among the simplest and most used to classify an image into many regions. In this context, the main objective of this work is to detect and locate precisely the damaged area in photovoltaic (PV) fields based on the clustering of a thermal image through the K-means algorithm. The clustering quality depends on the number of clusters chosen; hence, the elbow, the average silhouette, and NbClust R package methods are used to find the optimal number K. The simulations carried out show that the use of the K-means algorithm allows detecting precisely the faults in PV panels. The excellent result is given with three clusters that is suggested by the elbow method.

This paper presents an implementation of real-time energy management systems (EMS) to maximize the efficiency of the electricity distribution in an isolated hybrid microgrid system (HMGS) containing photovoltaic modules, wind turbine, battery energy storage system, and diesel generator (DG) which is used as a backup source. These systems are making progress worldwide thanks to their respect for the environment. However, hybridization of several sources requires power flow control (PFC). For this reason, in this work, a proper energy management system is developed using LabVIEW software and embedded in a suitable platform for the real-time management of the hybrid energy system. The developed EMS is tested and validated through a small-scale application which accurately represents the case study of an isolated mosque located in a remote area of Morocco. The aim of this paper is to (i) propose a novel modelling method and real-time monitoring interface under the LabVIEW software based on the real data obtained by an optimal sizing previously made using Homer-pro software and (ii) implement the power control system on a low-consumption embedded platform that is the Raspberry-pi3.

Diesel generators (DGs) or solar panels are widely used to provide electricity in isolated regions in Morocco. However, the instability of fuel prices and the stochastic nature of renewables imply that these systems cannot effectively supply the load demand with a good reliability. In this paper, an analysis of different energy components was performed in order to have a cost-effective and reliable energy system for supplying the load in an off-grid remote area. In the case studied, power evaluation of Hybrid off-grid System (HOS) shows that coupling wind turbines with PV panels decreases both total cost and pollution emissions of the system. Moreover, the Hybrid PV-Wind-Diesel system (PWDS) produces better results in comparison with PV-Diesel system (PDS), Wind-Diesel (WDS) and Diesel-only system (DS).

In this paper, a five-levels inverter is developed and used for injecting renewable power into the grid. To reduce harmonic distortion and increase the quality of the energy supplied by the PV system, we have used two filters. The goal is the elimination of the 3rd and the 5th harmonic by an adequate control applied to the five-levels inverter, also eliminate the 7th harmonics by an appropriate resonant filter. In a second time, we have used a low-pass filter for testing its performance comparatively to the above one. We calculated in different cases, spectrum and Total Harmonic Distortion (THD), both quantities necessary to judge the quality of the inverter output voltage. To validate our result we have used simulations with Psim.

A reduced complexity LDPC decoding method for regular LDPC code is extended to irregular LDPC codes; we present in this paper a full description of this method and its benefits for various row weight and length code word. The Split-Row method makes column processing parallelism easier to exploit and significantly simplifies row processors. Simulation results over an additive white Gaussian channel show that the error performance of high row-weight codes with Split-Row decoding is within 0.3-0.5 dB of the Min-Sum algorithm.

In this paper we present Low Density Parity Check decoding algorithm that assemble two different algorithms: Sum-Product and Bit-Flipping; we denote Bit Flipping-Sum Product Algorithm (BFSP). To reduce the bit error rate, we perform the Bit-Flipping algorithm after the Sum-Product algorithm. Simulation results over an additive white Gaussian channel show that the error performance of a LDPC codes with Bit Flipping-Sum Product decoding is within 0.2 dB of the standard Sum-Product decoding algorithms. Furthermore, the decoding complexity of the proposed BFSP algorithm is maintained at the same level as that of the standard Sum-Product algorithm.