Papers by Houssem Bouchekara
Nondestructive Testing and Evaluation, 2016
Abstract The aim of this paper is to propose a new efficient and reliable approach for the diagno... more Abstract The aim of this paper is to propose a new efficient and reliable approach for the diagnosis of wiring networks using Time Domain Reflectometry (TDR) and an Improved Black Hole (IBH) optimisation algorithm. A forward model based on RLCG parameters and Finite Difference Time Domain (FDTD) method is developed to simulate the TDR response for a given wiring network. The IBH algorithm is used to solve the inverse problem in order to detect, characterise and localise defects in wiring networks. Experimental studies are performed to show that the proposed approach can be implemented for the diagnosis of real-world systems. The results presented here demonstrate that the proposed approach using TDR and IBH is an efficient and reliable diagnosis approach for wiring networks.
... Le froid magnétique : modélisation et optimisation thermique de la réfrigération magnétique à... more ... Le froid magnétique : modélisation et optimisation thermique de la réfrigération magnétique à régénération active. Houssem Bouchekara 1 , Afef Kedous-Lebouc 1 , Cédric Dupuis 1 , Jean-Louis Coulomb 1 , Jean-Paul Yonnet 1. (15/05/2008). ... Contributeur : Sylvie Garcia <>. ...
Technologies, 2021
The localization of the nodes in wireless sensor networks is essential in establishing effective ... more The localization of the nodes in wireless sensor networks is essential in establishing effective communication among different devices connected, within the Internet of Things. This paper proposes a novel method to accurately determine the position and distance of the wireless sensors linked in a local network. The method utilizes the signal strength received at the target node to identify its location in the localized grid system. The Most Valuable Player Algorithm is used to solve the localization problem. Initially, the algorithm is implemented on four test cases with a varying number of sensor nodes to display its robustness under different network occupancies. Afterward, the study is extended to incorporate actual readings from both indoor and outdoor environments. The results display higher accuracy in the localization of unknown sensor nodes than previously reported.
In the emerging age of the Internet of Things (IoT), energy-efficient and reliable connection amo... more In the emerging age of the Internet of Things (IoT), energy-efficient and reliable connection among sensor nodes gain prime importance. Wireless engineers encounter a trade-off between sensors energy requirement and their reliable full connectivity. Consequently, the need to find the optimal solution draws the attention of many researchers. In this paper, the Electrostatic Discharge Algorithm (ESDA) is proposed, implemented, and applied to minimize energy needs of a sensor node while ensuring the fully-connectedness of each node. The obtained results show that the proposed method achieves better results than those found in the literature using the particle swarm optimization method in terms of energy savings and reliable connectivity.
─ Teaching-learning-based optimization (TLBO) is a rising star technique among metaheuristic tech... more ─ Teaching-learning-based optimization (TLBO) is a rising star technique among metaheuristic techniques with highly competitive performance. This technique, which has been recently introduced, is based on the effect of influence of a teacher on learners and learners on their colleagues. This paper intends to apply an improved version of TLBO in the field of electromagnetics. To demonstrate its effectiveness in this area, the proposed technique is applied to two benchmarks related to brushless direct current wheel motor problem and testing electromagnetic analysis methods problem number 22. The quality of the results presented shows that the proposed technique is very competitive with other well-known optimization techniques; hence, it is a promising alternative technique for optimization in the field of electromagnetics. Index Terms ─ electromagnetics, metaheuristics, optimization, teaching-learning-based-optimization.
IEEE Access, 2020
Wind farms are developed and implemented in many places around the globe. Designing a wind farm i... more Wind farms are developed and implemented in many places around the globe. Designing a wind farm is becoming more and more complex especially with the recent trend towards large farms. Finding the optimal locations of wind turbines inside a wind farm to reduce energy cost is a highly challenging task, as it requires the handling of conflicting criteria and depending on the number of turbines considered it can turn to a large scale-optimization problem. Therefore, the aim of this paper is to place efficiently wind turbines inside a given area considering all constraints. This problem formulated as an optimization problem is referred to as the wind farm layout optimization (WFLO) problem. This real-world problem is nonlinear and difficult to solve using classical optimization algorithms and it has to take into consideration wind scenarios, power curve and wake effects. For this purpose, a binary version of the most valuable player algorithm (MVPA) called BMVPA is developed and implemented. Furthermore, ten scenarios were investigated using different wind speeds, terrain sizes with and without obstacles. For the same terrain but including obstacles, it was found that the energy cost increased due to the presence of obstacles that could limit the search space and consequently reduces the number of available options. The empirical results obtained using BMVPA were compared with those obtained using other well-known algorithms like the binary particle swarm optimization and genetic algorithm. BMVPA showed better results in solving the WFLO problem than the comparative algorithms. The optimum design of the wind farm obtained will allow an efficient and economic exploitation of wind resource. INDEX TERMS Wind farm, layout design, wind energy, optimization.
IEEE Access, 2020
Since the last decade, power systems have been evolving dynamically due to smart grid technologie... more Since the last decade, power systems have been evolving dynamically due to smart grid technologies. In this context, energy management and optimal scheduling of different resources are very important. The main objective of this paper is to study the optimal scheduling of distributed energy resources (OSDER) problem. This problem is a challenging, complex and very large-scale mixed-integer non-linear programming (MINLP) problem. Its complexity escalates with incorporation of uncertain and intermittent renewable sources, electric vehicles, variable loads and markets which makes it hard to be solved using traditional optimization algorithms and solvers. However, it can be handled efficiently and without approximation or modification of the original formulation using modern optimization algorithms such as metaheuristics. In this paper, an improved version of the variable neighborhood search (IVNS) algorithm is proposed to solve the OSDER problem. The proposed algorithm was tested on two large-scale centralized day-ahead energy resource scenarios. In the first scenario, the 12.66 kV, 33-bus test system with a total of 49,920 design variables is used whilst in the second scenario, the 30 kV, 180-bus test system is used with a total of 154,800 design variables. The optimization results using the proposed algorithm were compared with five existing optimization algorithms, i.e., chaotic biogeography-based optimization (CBBO), cross-entropy method and evolutionary PSO (CEEPSO), chaotic differential evolution with PSO (Chaotic-DEEPSO), Levy differential evolution with PSO (Levy-DEEPSO), and the variable neighborhood search (VNS). For the first test system, the IVNS has achieved a score of-5598.89 while for the second test system it has achieved a score of-3180.15. A comparative study of the results has shown that the proposed IVNS algorithm performs better than the remaining algorithms for both cases.
IET Science, Measurement & Technology, 2019
An efficient diagnosis method dedicated to embedded wiring network based on reflectometry techniq... more An efficient diagnosis method dedicated to embedded wiring network based on reflectometry technique is developed in this study. The proposed methodology is based on the two complementary steps. In the first step, the time-domain reflectometry (TDR) method is simulated, by RLCG (R: resistance, L: inductance, C: capacitance and G: conductance) circuit model and the numerical finite-difference time-domain method, and at the same time the datasets are created. In the second step, the support vector machine (SVM) algorithm is combined with a principal component analysis to identify the faults on wiring network from the TDR response. Two types of SVM models have been used in the diagnosis procedure: SVM classifiers and SVM regression models. In order to illustrate the performances and the feasibility of the proposed approach, numerical and experimental results are presented.
Neural Computing and Applications, 2019
The main objective of this paper is to solve different configurations of the optimal power flow (... more The main objective of this paper is to solve different configurations of the optimal power flow (OPF) problem efficiently using an improved version of the newly proposed electromagnetic field optimization (EFO) algorithm. The developed and improved new version of EFO is based on chaotic maps and on a new mechanism. This improved version is called improved chaotic electromagnetic field optimization (ICEFO) algorithm. The performances of the ICEFO algorithm are evaluated on a large set of cases using: tow formulations, three objective functions (cost minimization, cost minimization and voltage profile improvement and cost minimization and voltage stability enhancement) and three test systems (the IEEE 30-bus, the IEEE 57-bus and the IEEE 118-bus test systems). The obtained results of the developed algorithm are compared with other well-known algorithms. These results demonstrate that the developed algorithm is able to solve efficiently different configurations of the OPF problem and for different test systems.
IET Energy Systems Integration, 2019
We propose here an extended attention model for sequence-to-sequence recurrent neural networks (R... more We propose here an extended attention model for sequence-to-sequence recurrent neural networks (RNNs) designed to capture (pseudo-)periods in time series. This extended attention model can be deployed on top of any RNN and is shown to yield state-of-the-art performance for time series forecasting on several univariate and multivariate time series.
Electric Power Systems Research, 2019
Abstract Electric spring has recently been proposed as a special type of reactive power compensat... more Abstract Electric spring has recently been proposed as a special type of reactive power compensator to improve various attributes of the smart grid. Its extended functionality and applications are yet to be explored. This paper presents a new application of electric spring to ensure constant power across the loading elements under impedance variations. Two control methodologies are proposed to carry out this task: a linear approximation configuration and a closed-loop configuration. Design and mathematical analysis are provided, and the results of both control configurations are analyzed for a simulation model as well as a hardware setup. It has been found that experimental results are congruent with those obtained from the simulation model, hence ensuring the efficacy of theoretical framework.
IET Science, Measurement & Technology, 2019
Because of their several advantages like simplicity, flexibility and adaptability, nature-inspire... more Because of their several advantages like simplicity, flexibility and adaptability, nature-inspired (NI) optimisation algorithms have attracted significant attention for solving complex optimisation problems. Source of inspiration for NI are multiple. This study aims to propose a new NI optimisation algorithm inspired by the electrostatic discharge (ESD) event. Tested on a large set of benchmarks and compared with several well-known optimisation algorithms the ESD algorithm (ESDA) is found to be a very competitive algorithm. Moreover, the ESDA has been applied for the determination of wort-case scenarios for electromagnetic compatibility (EMC) filter.
IET Renewable Power Generation, 2018
Computers & Electrical Engineering, 2014
ABSTRACT The performances of Particle Swarm Optimization and Genetic Algorithm have been compared... more ABSTRACT The performances of Particle Swarm Optimization and Genetic Algorithm have been compared to develop a methodology for wiring network diagnosis allowing the detection, localization and characterization of faults. Two complementary steps are addressed. In the first step the direct problem is modeled using RLCG circuit parameters. Then the Finite Difference Time Domain method is used to solve the telegrapher’s equations. This model provides a simple and accurate method to simulate Time Domain Reflectometry responses. In the second step the optimization methods are combined with the wire propagation model to solve the inverse problem and to deduce physical information’s about defects from the reflectometry response. Several configurations are studied in order to demonstrate the applicability of each approach. Further, in order to validate the obtained results for both inversion techniques, they are compared with experimental measurements.
Electromagnetics, 2014
ABSTRACT Time-domain reflectometry has proven to be one of the best methods for wiring network di... more ABSTRACT Time-domain reflectometry has proven to be one of the best methods for wiring network diagnosis, and it can easily be applied to the detection and localization of defects, while only requiring one access point to the wiring network. In this article, a novel approach combining the time-domain reflectometry response extracted from vector network analyzer measurements and the teaching–learning-based optimization technique is developed and applied to the diagnosis of wiring networks. The proposed approach consists of two steps. In the first step, propagation along the cables is modeled using the forward model. In the second step, teaching–learning-based optimization is used to solve the inverse problem to deduce physical information about the defects in the wiring network. The proposed approach has been successfully tested on several cases and for different configurations. Comparisons of the proposed time-domain reflectometry/teaching–learning-based optimization approach results with measurements reveal that this approach has a high potential and is very effective for wiring network diagnosis.
Renewable Energy, 2015
Resource optimization is a major factor in the assessment of the effectiveness of renewable energ... more Resource optimization is a major factor in the assessment of the effectiveness of renewable energy systems. Various methods have been utilized by different researchers in planning and sizing the grid-connected PV systems. This paper analyzes the optimal photovoltaic (PV) array and inverter sizes for a grid-connected PV system. Unmet load, excess electricity, fraction of renewable electricity, net present cost (NPC) and carbon dioxide (CO2) emissions percentage are considered in order to obtain optimal sizing of the grid-connected PV system. An optimum result, with unmet load and excess electricity of 0%, for serving electricity in Makkah, Saudi Arabia is achieved with the PV inverter size ratio of R = 1 with minimized CO2 emissions. However, inverter size can be downsized to 68% of the PV nominal power to reduce the inverter cost, and hence decrease the total NPC of the system.
Position Papers of the 2014 Federated Conference on Computer Science and Information Systems, 2014
Non-Destructive Testing (NDT) is an important area of research, dealing with diagnostic and monit... more Non-Destructive Testing (NDT) is an important area of research, dealing with diagnostic and monitoring the health of the electrical transmission networks and find automatically the failures. One of the recently developped (NDT) techniques is Time Domain Reflectometry methods, they are quite efficient for detecting important damages (hard faults), such as short or open-circuits. Interpreting the results obtained with reflectometry instrument for a wiring network requires great expertise, as the reflectometry response can be very complex. Morever, the reflectometry response it self is not self-sufficient to identify and localate the defects in cabling networks. There is the need to solve efficiently the inverse problem which consists of deducing some knowledge about the defects from the response at the input point of the network. In this paper, TDR and PSO algorithm have been combined and applied to produce a new sufficiently optimized method that permit the extaraction of damages informations from the time domain reflectograms. Finite Difference Time Domain (FDTD) method has been used to produce a training data set with the known of damages. The results obtained from the TDR-PSO algorithm confirmed the theoretical predictions, and gave us exact informations about the complexe structure's health.
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Papers by Houssem Bouchekara