M. BELHOUCHET Khaled

The main objective of this work is the implementation, in real time, of the scalar control for induction motor. To achieve this objective, we used a test bench capable of validating this type of control. this test bench contains a 2.2kw power induction motor, a three-phase voltage inverter (SMEKRON) and a modern dspace control board (Micro-labox) . We identified all the parameters of the motor in the first part and their verification in mathematical modeling under the MATLAB/ SIMULINK. The model presented under matlab represents exactly the induction motor that is deduced from the shape of the curves, current, flux ,torque and speed. The second part said the motor inverter association where' we have the closed loop driver using a three-phase inverter in use the PWM pulse width modeling technique sine-triangle. Experimental results a training has good dynamic

This research primarily focuses on the modeling and simulation of high frequency electromagnetic induction welding of pipes. The objective is to assemble two tubes of varying diameters. We have elucidated the electromagnetic and thermal mathematical models of our welding configuration and conducted a comprehensive analysis of the impact of various parameters on the quality of the welding process.

In this work, numerical modeling is used as a means of simulating the operating mode of a GaSb solar cell to convert solar energy into electrical energy. The structure of the chosen cell is of type p n. Numerical modeling is also used as a predictive tool for factors that can influence the mode of operation of the cell. The ordinary solar cell model was adopted to simulate the photocurrent, open-circuit voltage, form factor and efficiency. A thermal study is also carried out to simulate the effect of temperature and emissivity on the components of the TPV system. According to the results, we notice an increase in the characteristics as a function of the emissivity and the radiation temperature, and also a decrease in these characteristics under the effect of cell temperature.

This study delves into the development of porcelain using local raw materials, focusing on insulator contamination as a crucial factor contributing to flashover and security issues in high-voltage power systems. To alleviate this problem, the introduction of nano-TiO2 coating is proposed as a means to enhance the properties of porcelain insulators. This porcelain composition was formulated using a blend of kaolin, quartz, feldspar, and recycled waste glass. The resulting specimens underwent characterization through X-ray diffraction (XRD) and Fourier Transform Infrared Spectroscopy (FTIR) analyses. The research aims to evaluate the relationship between an insulator’s electrical performance and critical flashover voltage in the presence of nano-TiO2 coating, based on experimental results. An artificial contamination test was conducted, and characterization techniques such as Atomic Force Microscopy (AFM) were employed to analyze surface, morphology, and thickness. Raman spectroscopy was utilized to analyze the TiO2-coated surface. Electrical tests were performed on both coated and uncoated samples to assess the impact of the titanium dioxide film on electrical properties. The TiO₂ coating significantly enhances the reliability of porcelain insulators by increasing flashover voltage, reducing leakage current, and improving both dielectric strength and insulation resistance. Furthermore, the Finite Element Method (FEM) was applied to analyze the effects of coating on the porcelain insulator’s electrical performance. The results underscored the improvement in electrical properties of the studied porcelain, which can be attributed to the isolating properties of the nanoparticles. The combination of experimental and simulation results provides valuable insights into the influence of TiO2 thin film and its role in enhancing the electrical properties of porcelain.

This research examines the possibility of incorporating Machine Learning, a data-driven approach that learns from experience, into photovoltaic (PV) systems to significantly improve their efficiency and performance. Machine Learning offers a powerful and efficient way to solve complex problems by learning patterns and relationships from data. In this study, the authors explore the application of this technique in optimizing critical parameters within PV systems, such as module orientation, tilt angle, and power management. The research presents a comprehensive analysis of Machine Learning’s performance when applied to various photovoltaic systems in varying environmental conditions and load demands. The results demonstrate that the integration of Machine Learning leads to substantial improvements in system efficiency, energy output, and overall performance. This is achieved by optimizing the PV system’s parameters to maximize energy generation while minimizing energy losses and maintaining stability under fluctuating load conditions. Additionally, the paper discusses the advantages of using Machine Learning over traditional optimization techniques, such as its ability to manage problem optimization issues that are nonlinear and non-convex in nature, effectively. The authors also highlight the potential for further research in this area, including the exploration of other data-driven optimization techniques and the development of advanced control strategies for PV systems.

High-voltage insulator has an important role in ensuring the reliability and longevity of electrical systems. This study investigates the optimization of corona ring design and parameters H,R,r, using the Grey Wolf Optimizer (GWO), aiming to minimize electric field concentrations and enhance insulator performance. By strategically placing corona rings, the model developed predicts optimal configurations that mitigate peak electric field levels, thereby improving durability and preventing unexpected failures. Numerical simulations, conducted via COMSOL 4.3 and Finite Element analysis, validate the effectiveness of the optimized design in significantly reducing electric field intensities near end fittings. This research underscores the transformative potential of AI-driven optimization techniques, particularly the GWO, in advancing predictive maintenance strategies for high voltage insulators. Such advancements are crucial for fostering a sustainable and resilient electrical infrastructure.

insulators with High voltage are the key components in energy transmission systems wich may be affected by pollution. In this paper, the
performance of a cap and pin insulator is investigated with the objective of predicting the propagation of an AC surface arc on contaminated insulators
in the presence of a non- uniform electric field. To assess the performance of the insulator, the electric field simulation is carried out by using the finite
element method. Research results show that non-uniform contamination had the worst electric field profile. This work can provide a useful tool for the
design and maintenance of cap and pin insulators.

This study investigates the flashover voltage on high voltage insulators in polluted environments, employing a combination of experimental and theoretical approaches. Utilizing Analysis of Variance (ANOVA) and the Finite Element Method (FEM), the research aims to offer a thorough comprehension of the phenomenon. The experimental aspect of the study involves subjecting insulators to polluted environments, replicating real-world scenarios, and measuring the resulting flashover voltages. This experimental study focuses on investigating flashover pollution, where artificial pollution is introduced into an experimental model. An observational approach is employed to assess the impact of conductivity and pollution distribution on the 1512 L cap and pin insulator, as well as its proposed experimental model. Complementing the experimental approach, the theoretical aspect of the study employs the FEM method to simulate and model the insulator's behaviour under pollution. This computational technique enables a detailed analysis of the electrical field distribution, surface potential, and stress repartition across the surface of the insulator. Simulations further explore the impact of pollution on flashover voltage and leakage current. The FEM results are then compared with experimental findings to validate the model's accuracy and reliability. By analyzing the collected data, the ANOVA technique is applied to identify significant differences in flashover voltage under diverse pollution levels. This statistical approach allows for the determination of the most influential factors affecting insulator performance. This research offers valuable insights into the influence of flashover voltage on high-voltage insulators in polluted environments, contributing to the development of more robust and efficient insulation systems. The combination of ANOVA and FEM methods provides a robust framework for understanding and predicting insulator performance, ultimately benefiting the power industry and promoting energy sustainability.

The distribution of the electric field plays a crucial role in evaluating the effectiveness of composite insulators. This research presents an innovative approach for optimizing the design of corona rings, aiming to decrease the electric field intensity in critical areas to satisfactory levels. The investigation examined the correlation among corona ring design variables and the intensity of the electric field close to the energized-end fitting in a 220 kV composite insulator equipped with a corona ring. Using design of experiment methods, the impact of three corona ring design parameters – corona ring diameter (R), ring tube diameter (Dr), and corona ring height (H) – was assessed. A novel nonlinear mathematical objective function was formulated, connecting the electric field magnitude to the structural parameters of the corona ring. Subsequently, the Bat algorithm, a bio-inspired optimization technique, was employed to enhance the initial corona ring design and mitigate the electric field intensity. Finally, the Finite Element Method (FEM) was utilized to simulate and evaluate the voltage distribution and electric field stress. The optimization process resulted in a substantial decrease in the highest electric field magnitude, by 58.6 % compared to the insulator with the threshold value, and 75 % when devoid of a corona ring. Additionally, the research highlighted the significant influence of ring tube thickness on the distribution of the electric field. The combination of experimental design and the Bat algorithm proved to be a powerful tool for optimizing the design of corona rings on transmission line composite insulators, providing precise solutions for optimizing corona discharge problems and enhancing the reliability of power transmission systems.

Dual stator winding Induction machine (DSWIM) is widely used in industrial and transportation applications which
are essential to industrial processes. However, the unreliability and unpredictable life cycles of these machines still present
opportunities and challenges for condition monitoring research. A machine breakdown leads to costly repairs and high losses
due to downtime. The motivation of this research is to improve the reliability of DSWIM through non-invasive methods for
condition monitoring. To monitor and detect these faults at the initial stage, this thesis proposes condition monitoring based on
the magnetic stray flux. By studying the interaction between stray flux variations and machine failure, different types of faults
can be classified and distinguished through numerical simulation by Finite Element Method simulations (FEM) and Signal
Processing Technique (FFT).

This paper presents a comprehensive investigation into the electrical equivalent circuits applicable to contaminated cap and pin insulators. The study aims to provide a better understanding of the impact of contamination on insulator performance and develop effective strategies for mitigating these effects. The primary focus of the investigation is the development and analysis of electrical equivalent circuits that accurately represent the behavior of contaminated insulators. These circuits consider various factors such as surface contamination and aging, which can significantly influence the insulator's performance. The study employs theoretical modeling validation techniques to ensure the accuracy and reliability of the developed circuits.
The results obtained from this investigation demonstrate the effectiveness of the proposed electrical equivalent circuits in predicting the behavior of contaminated cap and pin insulators. The circuits can be utilized in the design and maintenance of electrical power systems to enhance insulator performance and reduce the risk of power outages and equipment failures. Furthermore, the study highlights the importance of continuous research and development in addressing the challenges posed by insulator contamination in modern power systems.

In this work, a study on a cap and pin insulator (1512L) is proposed to evaluate the distribution of the electric field and the potential along the insulator under different conditions. A computational and experimental study for the examination of a real insulators model is assessed. Tests on contaminated insulators in the laboratory under the suggested conditions have been carried out. Finite element methods (FEM) have been employed in the numerical analysis to assess the electrical properties of the insulator under the suggested contamination profiles, including potential and electric field. The study proposed in this paper provides an effective and practical tool for analysis and enhance the dielectric properties of the studied insulator.

Optimization of hydrogen production using MPPT algorithms in a PV system

Increased focus on contaminated insulators at high voltage transmission lines has emerged due to their growing significance. This pollution may cause flashover voltage, potentially resulting in service interruptions and negatively impacting the power system's dependability. The occurrence of flashovers in power transmission line insulators poses a significant risk to the efficient functioning of power systems. This study aims to address the issue of predicting flashovers in composite insulators employed in power transmission lines. It achieves this by utilizing a neural network technique. The present research examines the utilization of Artificial Neural Networks (ANN) to forecast the flashover characteristics of composite insulators influenced by weather and environmental factors. The training data for ANN was sourced from tests conducted in a climate chamber under high voltage stress. The predicted parameters in this research include, flashover voltage, and surface resistance. The proposed ANN model offers a valuable tool for accurately predicting the flashover parameters of composite insulators affected by extreme humidity, and pollution levels. These findings will also contribute to a better comprehension of the flashover process in outdoor composite insulators.

Photovoltaic systems rank among the top renewable energy solutions. A significant disadvantage is their high initial investment. To reduce costs, optimizing the system's size is a viable approach. This paper aims to explore the potential of Particle Swarm Optimization (PSO) in enhancing the efficiency of independent solar power systems. Independent solar power systems are crucial for sustainable energy generation, particularly in remote locations or off-grid settings. The study investigates the application of PSO, a nature-inspired computational technique, to optimize various factors affecting solar power system performance, such as panel orientation, tilt angle, and energy storage management. By employing PSO algorithms, this research seeks to identify the optimal configuration that maximizes energy output while ensuring cost-effectiveness and minimal environmental impact. The results obtained from this study can significantly contribute to the advancement of renewable energy technologies and promote global efforts towards sustainable development and energy security.

This work aims to use artificial intelligence to predict the flashover voltage of a high-voltage insulator that has been artificially polluted. The optimum parameters of the mathematical model that explains this phenomenon can be identified using the Bat Algorithm (BA). Next, two different bat algorithms are created to create a nonlinear model that connects the essential flashover voltage to the previously listed features. The COMSOL Multiphysics software program has been utilized in the development of the tools and algorithms. The efficiency of using the Bat Algorithm technique for optimization and modeling of crucial flashover voltage and leakage current against contamination is fully demonstrated by further comparing the estimated results with the measured data gathered from the site measurement. In order to carry out the analysis, a statistical model is presented to address the relationship between the parameters of the insulator layer (the position and conductivity of the pollution layer) and the pollution. In this research, these factors are changed on a regular basis. An analysis of variance (ANOVA) is a statistical method used to evaluate the effect of every parameter. The response surface methodology (RSM) models the link between surface firing, maximum electric field, breakdown intensity, and the selected parameters. The results demonstrate the applicability of artificial intelligence in high-voltage transmission line simulation. The technique presented in the research can be applied to offer an efficient remedy against pollution flashover of high voltage insulators, as well as to eradicate the flashover fault and establish maintenance strategy.

Heat transfer is a fundamental science that deals with the rate of energy transfer
thermal. In engineering practice, understanding the transfer mechanisms of heat
is becoming increasingly important, as heat transfer plays a crucial role in the
design of vehicles, power plants, refrigerators, electronic devices, buildings and
bridges, among others. Even a chef must have an understanding intuitive heat
transfer mechanism to cook food “properly” adjusting the heat transfer rate. We
may not know it, but we already use the principles of heat transfer to seek
thermal comfort. We isolate our body by putting on heavy coats in winter, and
we minimize heat gain by radiation by staying in shaded places in summer. We
accelerate the cooling hot food by blowing on it and keeps us warm in cold
weather by huddling and thus minimizing the exposed surface area.

The integration of renewable energies, particularly photovoltaic energy, into green hydrogen production presents a highly promising prospect in the energy sector. Nonetheless, these energy sources face challenges due to their inherent instability and susceptibility to various atmospheric factors such as temperature and illumination. Therefore, it's imperative to tackle these challenges before renewable energy can be widely adopted as a primary source in hydrogen production. To address this, we propose constructing an autonomous photovoltaic system using Matlab software. This system will employ a DC-DC boost converter to connect the PV array to the load. Furthermore, to enhance the efficiency of photovoltaic power generation, we will implement the perturbation and observation maximum power point tracking (MPPT) approach. The research endeavor extends towards integrating this optimized system with an electrolysers developed a sophisticated electrolyte model utilizing MATLAB Simulink software, paving the way for hydrogen gas production.

La planification des réseaux peut être définie comme l’anticipation des futurs besoins en lignes, câbles et postes d’un système électrique, dans le but d’adapter les réseaux aux évolutions du système, et ce de manière optimale d’un point de vue technico-économique. Elle concerne à la fois le raccordement de nouvelles unités de production ou de consommation, le renforcement des infrastructures existantes et la création de nouveaux ouvrages. Pour être effective, la planification doit également prendre en compte les interactions entre ces futures installations. La planification des réseaux électriques consiste à prévoir les nouvelles centrales de production, les différentes extensions du réseau ainsi que le dimensionnement des ouvrages tels que les lignes, les transformateurs et les postes. Les centrales de production sont réalisés en considérant les ressources énergétiques qui sont riches et variées vue l’étendue géographique et climatique de notre pays. La planification du réseau électrique prend en compte un certain nombre d'objectifs qui doivent être optimisés simultanément et qui sont souvent contradictoires. Ces objectifs comprennent d'une part la minimisation des coûts d’exploitation (les pertes dans les lignes) et des coûts d’investissement (réalisation d’ouvrages), d'autre part l'amélioration de la fiabilité, la sécurité des personnes et des biens, la qualité et la continuité de fourniture et la considération de multiples facteurs environnementaux.
Ce polycopié est un support pédagogique de cours de planification des réseaux électriques destiné aux étudiants de première année master spécialité : électrotechnique, option : Réseaux électriques. Ce manuel constitue un recueil enrichi et détailler de l’ensemble des parties importantes caractérisant un réseau électrique ainsi que les principes et les règles de la planification des réseaux électriques.
L’objectif de ce module est de former l’étudiant dans la description de la planification des réseaux électriques pour lui permettre de maitriser les questions de planification des réseaux électriques à court, à moyen et à long terme, principalement l’extension de la production, du transport et de la distribution ainsi que la planification de l’énergie réactive.

Ce polycopié est un support pédagogique de travaux pratiques de la matière de méthodes numériques destiné aux étudiants de deuxième année de socle Sciences et Techniques (ST) ; toutes spécialités confondues. Ce manuel offre la possibilité d’implémenter en « MATLAB » quelques méthodes étudiées dans les cours de méthodes numériques.
Ce support, a pour objectif d’approfondir les connaissances en programmation sous MATLAB des méthodes numériques et utiliser ce logiciel pour implémenter, analyser et comparer les résultats.

This work attempt to elucidate the potentials of Artificial Neural Networks (ANNs) in high voltage applications and especially to estimate the flashover voltage on polluted insulators, The importance of the research on insulator pollution has been increased considerably with the rise of the voltage of transmission lines. In order to determine the flashover behavior of polluted high voltage insulators and to identify to physical mechanisms that govern this phenomenon, the researchers have been brought to establish a modeling. Artificial neural networks (ANN) have been used by various researches for modeling and predictions in the field of energy engineering systems. In this study, model of UC = f (H, D, L,σ, d, n) based on ANN which compute flashover voltage of the insulators were performed. This model consider height (H), diameter (D), total leakage length (L), surface conductivity (σ ) and number of shed (d) of an insulator and number of chain (n) on the insulator.

Le travail présenté vise à étudier l'effet des doubles anneaux corona autour de l'extrémité sous tension d'un isolant composite 400KV. L'analyse est basée sur la méthode des éléments finis (FEM) et les résultats obtenus grâce aux simulations à l'aide du logiciel COMSOL Multiphysics. L'analyse de variance (AVOVA) permet d'évaluer les principaux effets et les effets d'interaction de trois paramètres étudiés; position de l'anneau à partir de l'extrémité, rayon de l'anneau et épaisseur du tube de l'anneau. La relation entre le champ E et les paramètres géométriques est optimisée et modélisée par la méthodologie de surface de réponse (RSM). Les résultats montrent une optimisation des champs électriques, et une distribution uniforme du potentiel électrique a été montrée. La méthodologie proposée est efficace pour concevoir des anneaux corona sur des isolateurs composites de lignes de transmission et présente un résultat plus précis pour trouver les meilleures solutions aux problèmes de décharges corona.

This article is interested in the study of the efficiency of a semiconductor material composed of zinc oxide for the control of the stresses on the external polymer insulations. Numerical modeling and simulation studies have examined stress-induced degradations as a cause of aging and insulation defects, the determination of electric field distributions, considerations for outdoor insulator modeling and field-optimization techniques for achieving stress relief. A short length microvaristor coating has been introduced at both ends of the insulator to control the high field, especially near the high voltage and ground terminals. The introduction of a microvaristor material with an appropriate switching characteristic has led to a marked improvement in the electric field and heat distributions along the insulator profile.

The influences of the pollution layer parameters including; conductivity, position and length on
the performance of high-voltage cylindrical insulator were investigated. Parameters effects and their
interactions have been assessed and determined using the variance statistical technique and the
relation between parameters and the flashover voltage, maximum electric field and the breakdown
strength is modeled by the response surface methodology (RMS). The 3D model from Comsol
Multiphysics was used for modeling and the FEM method was utilized for simulations. The findings
demonstrate that the flashover voltage of the non-uniformly contaminated surface is primarily
affected by the pollution layer length. Simulation results show that the intensity of the electric field
rises with the increasing in length of pollution layer and its position. It was noted that the
experimental tests in laboratory for non-uniform contamination are in strong alignment with
simulation studies. The results of this analysis should expand our understanding about the
performance of outdoor insulators under specific contaminated conditions. The knowledge gathered
can be used to enhance the configuration of insulators used in contaminated regions and it is
believed that the current study has resulted methodology to estimate reliably and realistically the
pollution performance of cylindrical porcelain insulators.

In this paper, a novel application of a Nano‐sized titanium dioxide photocatalyst for enhancing the performance of the photovoltaic panels is presented. The concept of cleaning solar panels using Nano coatings is proposed in this study to avoid the collection of fine particles, dust and water from the air above the solar panel that prevents sunlight from reaching the surface of the solar cell. The chemical and physical and properties of the TiO2 thin film were analyzed and their influences on the self‐cleaning were evaluated. In order to create self-cleaning property, the TiO2 films were deposited on sample surfaces. The samples were exposed then, to the outdoor conditions tests. Simulations studies were carried out for two different cases; with coating and without coating. Test results showed that the as‐prepared TiO2‐coated samples exhibited an improved self‐cleaning ability of the excellent photo‐induced catalytic performance of the TiO2 film. These surface modifications have a significant impact in the reduction of dust accumulation and the loss of the transmission coefficient. This study shows that coating TiO2 films on the surface of photovoltaic panels may have a promise in improving its performance in terms of temperature and efficiency. It can be effectively use the concept of Nano Anti-contamination self-cleaning of photovoltaic panels.

Aging of metal oxide surge arresters is mainly affected by the high electric stress near the high voltage electrode. It has been observed that the distribution of voltage and electric field along an arrester is non-uniform. In this paper, the effect of grading ring geometrical parameters is investigated in order to achieve a more uniform electric field and voltage distribution. A 3D finite element method was employed based on COMSOL MULTIPHYSICS software to calculate these latter. The optimal design of grading ring is achieved by minimizing the electric field along the active column of varistors using a reduced number of simulations based on the Taguchi design and a bat algorithm. Initially, simulation results for a single grading ring were analysed. By using an optimal design of the single ring, the electric field is reduced by 42% in the top of the 220 kV metal oxide surge arrester and the voltage distribution can be improved. It was proposed to install an additional ring at the top of the metal oxide surge arrester. Simulation results shows that using of an optimal design of grading rings, the distribution of voltage and electric field along the surge arrester can be made as uniform as possible

Ce travail présente la conception et l'optimisation de l'anneau corona pour un isolateur composite 400KV afin de minimiser les décharges corona. Ce travail a traité l'effet des dimensions de l'anneau corona en utilisant l'approche d'optimisation de l'algorithme Bat. Cette technique est établie pour dériver la fonction objective entre les paramètres de champ E maximum et d'anneau corona. La méthode des éléments finis (FEM) est appliquée pour calculer le champ électrique et la distribution de potentiel ainsi que pour optimiser la conception de l'anneau corona. Les résultats de la simulation montrent que l'utilisation de l'anneau corona optimisé conduit à une réduction significative de l'intensité du champ électrique côté HT et à des distributions de potentiel plus uniformes. L'application de l'algorithme de chauve-souris pour ce problème donne les dimensions optimales de Dr et R ainsi que la position H qui améliorera le plus efficacement la valeur du champ électrique.

In this paper, we present an optimisation method based on genetic algorithms and artificial neural networks (ANN) experimental data from artificially polluted insulators for the determination of the arc constants and dielectric properties in the surface. The study of flashover phenomenon in polluted insulators has not yet been described accurately through a mathematical model. The definitions of arc constants are very difficult, which is created in the dry bands when the voltage exceeds its critical value. In this work, a pollution flashover generalised model is used. The obtained results show that the mathematical model with optimised arc constants simulates accurately the experimental data and corroborate the inverse relationship between flashover voltage and pre-flashover leakage current. For this purpose, an ANN was constructed in MATLAB and has been trained with several MATLAB training functions, while tests regarding the number of neurons, the number of epochs and the value of learning rate have taken place, in order to find which net architecture and which value of the other parameters give the best result. To validate our method an experimental tests for different insulators show very good agreement with the measured values and the computed ones.

In this work, the effect of heating rate and mechanical activation on the reaction of kaolin and aluminium powder was investigated. A batch comprised of 89.5 wt%kaolin and 10.5 wt%aluminiumpowders was mixed and milled in a planetary ball-mill for 1, 5, 10, 20 and 40 h. The mixture powders were heat treated with a heating rate of 5, 10, 15, 20, 30 and 40 °C/min, respectively. After milling for 20 and 40 h, the results showed the formation of free silicon, quartz and nacrite (Al2Si2(OH)4) at room temperature. The kaolinite dehydroxylation, aluminium oxidation and the θ- to α-Al2O3 transformations are highly affected by heating rate and mechanical activation. As comparedwith the smallest heating rate, the mixtures heated with faster heating rate show the disappearance of the peak corresponding to the oxidation of aluminium and the appearance of a second peak corresponding to the formation of α-Al2O3. The intensity of the last peak increases with increasing of the heating rate and milled at lower milling time. The effects of heating rate in the reaction of kaolin and aluminium powder are attributed to the amorphization of kaolinite, the diffusion of Al3+ to form an amorphous alumina layer on the particle surface and the generation of microcracks at the particle surface of aluminium powder.

tThe present study is aimed to develop porcelain from locally available raw materials. Thisporcelain was prepared from the mixture of kaolin, quartz, feldspar, and recycled wasteglass. In this work, the expensive K-feldspar was substituted by recycled waste glass derivedfrom broken car glass. The effects of recycled waste glass in partial replacement of K-feldsparfor porcelain are discussed. Experimental results showed significant effects of recycledwaste glass substitution and sintering temperature on physical properties. Furthermore,the microstructure observation indicated that the replacement of K-feldspar by the recy-cled waste glass indicates the reducing firing temperature 200◦C was achieved by 30 wt%glass addition. Moreover, experimental investigations showed excellent mechanical (micro-hardness) and insulating properties (dielectric strength) of the prepared porcelain whencompared to that of traditional porcelain insulators. The Vickers micro-hardness found anincrease with both glass addition and sintering temperature. Dielectric constant (ε), dielec-tric loss tangent (tan ı) and loss factor (ε) were measured at different frequencies. Theresults reveal that glass addition enhances the dielectric properties of the samples fired at1100◦C. Finally, the best results of phase angle were obtained ∼(−89.2◦) for this porcelain.These results prove that our prepared insulator is a dielectric capacitor.

In this paper, a characterized transient finite
element analyses for permanent magnet (PM) dimension
and performance simulation of a V-permanent magnet
shape interior synchronous motor (V-IPMSM) with 8 poles
is developed. The relationship between induced
electromotive force (EMF) and PM dimension is analyzed;
the dimensions of permanent magnet are optimized by using
Particle Swarm Optimization (PSO) method, and the
performances of the sample IPMSM are simulated by using
MAXWELL-3D software. This proposed stochastic
algorithm finds the optimized solution with fast
computation and high convergence.

The design process of a double-sided slotted
TORUS axial-flux permanent-magnet (AFPM) motor
suitable for direct drive of electric vehicle (EV) is
presented. It used sizing equation and Finite Element
Analysis (FEA). AFPM motor is a high-torque-density
motor easily mounted compactly onto a vehicle wheel,
fitting the wheel rim perfectly. A preliminary design is a
double-sided slotted AFPM motor with 6 rotor poles for
high torque-density and stable rotation. To obtain, with
the highest possible torque, the initial design parameters
of the motor, AFPM's fundamental theory and sizing
equation were applied. Ansoft Maxwell-3D 12.0
commercial software ran the FEA of the motor design,
evaluating and enhancing accuracy of the design
parameters. Results of the FEA simulation were
compared with those obtained from the sizing equation;
at no-load condition, the flux density at every part of the
motor agreed. The design process is comprehensive and
can be used for an arbitrary EV with an arbitrary
cruising scenario.

Flashover phenomenon in polluted insulators has not yet been described accurately through a mathematical model. The main difficulty lies in the definition of arc constants, which is formed in the dry bands when the voltage exceeds its critical value. The authors present an optimization method based on genetic algorithms with real coding and experimental data from artificially polluted insulators for the determination of the arc constants. In this work a pollution flashover generalized model is used. The obtained results show that the mathematical model with optimized arc constants simulates accurately the experimental data.

In this paper, we present an optimisation method based on genetic
algorithms and artificial neural networks (ANN) experimental data from
artificially polluted insulators for the determination of the arc constants and
dielectric properties in the surface. The study of flashover phenomenon in
polluted insulators has not yet been described accurately through a
mathematical model. The definitions of arc constants are very difficult, which
is created in the dry bands when the voltage exceeds its critical value. In this
work, a pollution flashover generalised model is used. The obtained results
show that the mathematical model with optimised arc constants simulates
accurately the experimental data and corroborate the inverse relationship
between flashover voltage and pre-flashover leakage current. For this purpose,
an ANN was constructed in MATLAB and has been trained with several
MATLAB training functions, while tests regarding the number of neurons, the
number of epochs and the value of learning rate have taken place, in order to
find which net architecture and which value of the other parameters give the
best result. To validate our method an experimental tests for different insulators
show very good agreement with the measured values and the computed ones.

the effect of soda lime silica waste glass, from the glass ca broken in partial replacement of k-feldespar for porcelain insulators are disscussed. in this study, different mixes of four different materials namely; Kaolin from Tamazert, quartz from Tamerasset feldespar from Spain and recycle soda-lime-silica were prepared by sintering the samples between 1000 and 1300°C for 2 h. Mesearments have been performed by X-ray diffraction, scanning electron microscopy, DTA/TG, water absorption, and mechanical tests. Experimental results showed a significant improvement in physical and michanical properties. the microstructure observasion indicated that replacement of K- feldespar by the soda-lime-silica waste can facilate the sintering of the porcelain insulators. Finally, the addition of the soda-lime-silica improves the dielectric properties.

This paper presents the optimization design and analysis of axial flux permanent-magnet (AFPM) machine (internal stator external rotor) used in flywheel energy storage system (FESS). The main idea of design is reduce the pressure and friction acting on the lower bearing system, thus reducing the bearing losses and, therefore, the self-discharge of the stored energy during the standby mode. Due to the unconventional flux path distribution of this machine, a 3-D finite element (Maxwell 12) method was used to analyses the design, of course including its electromagnetic torque and axial force performances. The effects of the rotor permanent magnet (PM) and slots skew angles on the cogging torque and the axial force have been studied. It is found that an optimum skew angle is effective in reducing the overall cogging torque with negligible effect on the static axial force. The latter is essential as it can be utilized to minimize the axial bearing pressure in FESS application.

Flashover phenomenon in polluted insulators has not yet been described accurately through a mathematical model. The main difficulty lies in the definition of arc constants, which is formed in the dry bands when the voltage exceeds its critical value. We have present an optimization method based on genetic algorithms and Artificial Neural Networks (ANN) experimental data from artificially polluted insulators for the determination of the arc constants and Dielectric properties in the surface. In this work a pollution flashover generalized model is used. The obtained results show that the mathematical model with optimized arc constants simulates accurately the experimental data and Corroborate the inverse Relationship between flashover voltage and pre-flashover leakage current. For this purpose, an ANN was constructed in MATLAB and has been trained with several MATLAB training functions, while tests regarding the number of neurons, the number of epochs and the value of learning rate have taken place, in order to find which net architecture and which value of the other parameters give the best result.

Flashover phenomenon in polluted insulators has not yet been described accurately through a mathematical model. The main difficulty lies in the definition of arc constants, which is formed in the dry bands when the voltage exceeds its critical value. We have present an optimization method based on genetic algorithms and Artificial Neural Networks (ANN) experimental data from artificially polluted insulators for the determination of the arc constants and Dielectric properties in the surface. In this work a pollution flashover generalized model is used. The obtained results show that the mathematical model with optimized arc constants simulates accurately the experimental data and Corroborate the inverse Relationship between flashover voltage and pre-flashover leakage current. For this purpose, an ANN was constructed in MATLAB and has been trained with several MATLAB training functions, while tests regarding the number of neurons, the number of epochs and the value of learning rate have taken place, in order to find which net architecture and which value of the other parameters give the best result.

today, the magnet machines are important in industrial applications where low power is desired compactness, hardiness and good yield (robotics, appliances, etc ....) Medium power (machine tools, electric vehicles, aerospace, etc ....) for variable speed drives as well as the great powers in particular in rail traction, marine propulsion and the production of electrical energy (wind turbines). Also there are several special features of axial flux machines. First, it can be designed to have a power to weight ratio higher with substantial savings of materials used. Second, the axial flux machine usually has a greater length-diameter ratio. Third, the topology of the magnetic circuit of the machine axial flux can be easily changed in such a way that various types of axial flux machines can be designed according to the number of wishing floor. In addition to this topology is favored because the magnetic circuit is without notches.