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The work presented involves the multiphysical modelling, simulation and design optimization of a key component of a Solar Selective Coatings (SSC). The investigated SSC absorber consists of a near homogeneous distribution of nanoparticles of Titanium Nitride (TiN) in a matrix of Aluminium Nitride (AlN), to form a composite dielectric. With the aim of achieving high absorbance in the visible region of the spectrum and minimum reflectance in the infrared region of the spectrum, our work highlights the numerical design, the synthesis and optical characterization of a composite dielectric of approximately 500 nm thickness. A bottom-up approach for the preparation of a stack with alternate layers, consisting of a distribution of TiN nanoparticles with a layer of AlN on top, was adopted. The TiN nanoparticles, laid on a substrate (Silicon/Glass) by wet chemical method, are coated with conformal layer of AlN, via Plasma-enhanced Atomic Layer Deposition (PE-ALD). The control of the morphology at the nanoscale is fundamental in improving the optical performance of the material. For this reason, two composites were prepared. One starting with TiN dispersions made with dry TiN powder and deionized water, and the other with ready-made TiN dispersions. In both composites, the particles were 20–30 nm in diameter. In both the cases, fewer clusters of about 0.5–1 μmof TiN particles were present however, enough steps were taken to minimize these clusters into smaller particles. Parameters, such as the size of TiN nanoparticles, the thickness of AlN thin film, were revealed by the numerical simulations, performed using Wave-Optics module in COMSOL Multiphysics. The work showcased clearly compares the two kinds of composites, using scanning electron microscope, X-ray photoelectron spectroscopy and electrical conductivity measurement. In addition, the optical performance of the two prepared composites is used as a means of validating the computational model.

Reducing greenhouse gas emissions becomes a top priority in the world with the emergence of global warming and environmental problems. Various renewable energies appear during the last decades. Ocean captures and stores huge amounts of energy, which could satisfy five times of world energy demand. Due to technology limitations and economic considerations, marine current energy appears the most attractive choice compared with the other ocean energy form. Although the existing expertise and technology in offshore wind energy conversion system can be partially transferred to marine current energy conversion system due to the similar structure, there are still many technological challenges to overcome. Meanwhile, the system operates under the water will inevitably have some negative or positive impacts on the surrounding environment. In this paper, it shows the interest and the principle of the marine current energy, and also discusses the advantages and disadvantages. The environmental impacts around the devices, the technological challenges, and the essential support structures are presented as well. The state-of-the-art horizontal axis turbines and their relative technologies and the latest projects are described finally. This review paper gives the useful information about the attraction and challenge of the marine current energy, and the newest development of the technologies and projects.

This paper presents torque ripple reduction with speed control of 8/6 Switched Reluctance Motor (SRM) by the determination of the optimal parameters of the turn on, turn off angles Theta_(on), Theta_(off), and the supply voltage using Particle Swarm Optimization (PSO) algorithm and steady state Genetic Algorithm (ssGA). With SRM model, there is difficulty in the control relapsed into highly non-linear static characteristics. For this, the Finite Elements Method (FEM) has been used because it is a powerful tool to get a model closer to reality. The mechanism used in this kind of machine control consists of a speed controller in order to determine current reference which must be produced to get the desired speed, hence, hysteresis controller is used to compare current reference with current measured up to achieve switching signals needed in the inverter. Depending on this control, the intelligent routing algorithms get the fitness equation from torque ripple and speed response so as to give the optimal parameters for better results. Obtained results from the proposed strategy based on metaheuristic methods are compared with the basic case without considering the adjustment of specific parameters. Optimized results found clearly confirmed the ability and the efficiency of the proposed strategy based on metaheuristic methods in improving the performances of the SRM control considering different torque loads.

This paper presents system modeling, analysis, and simulation of an electric vehicle (EV) with two independent rear wheel drives. The traction control system is designed to guarantee the EV dynamics and stability when there are no differential gears. Using two in-wheel electric motors makes it possible to have torque and speed control in each wheel. This control level improves EV stability and safety. The proposed traction control system uses the vehicle speed, which is different from wheel speed characterized by a slip in the driving mode, as an input. In this case, a generalized neural network algorithm is proposed to estimate the vehicle speed. The analysis and simulations lead to the conclusion that the proposed system is feasible. Simulation results on a test vehicle propelled by two 37-kW induction motors showed that the proposed control approach operates satisfactorily. Index Terms--Electric vehicle (EV), induction motor, neural networks, speed estimation, traction control.

This paper addresses the problem of controlling power generation in variable-speed wind energy conversion systems (VS-WECS). These systems have two operation regions depending on the wind turbine tip-speed ratio. They are distinguished by minimum phase behavior in one of these regions and a nonminimum phase in the other one. A sliding mode control strategy is then proposed to ensure stability in both operation regions and to impose the ideal feedback control solution despite model uncertainties. The proposed sliding mode control strategy presents attractive features such as robustness to parametric uncertainties of the turbine and the generator as well as to electric grid disturbances. The proposed sliding mode control approach has been simulated on a 1.5-MW three-blade wind turbine to evaluate its consistency and performance. The next step was the validation using the National Renewable Energy Laboratory (NREL) wind turbine simulator called the fatigue, aerodynamics, structures, and turbulence code (FAST). Both simulation and validation results show that the proposed control strategy is effective in terms of power regulation. Moreover, the sliding mode approach is arranged so as to produce no chattering in the generated torque that could lead to increased mechanical stress because of strong torque variations. Index Terms--Power generation control, sliding mode control, wind energy conversion system.

This paper investigates the use of fuzzy logic for fault detection and diagnosis in a pulsewidth modulation voltage source inverter (PWM-VSI) induction motor drive. The proposed fuzzy technique requires the measurement of the output inverter currents to detect intermittent loss of firing pulses in the inverter power switches. For diagnosis purposes, a localization domain made with seven patterns is built with the stator Concordia current vector. One is dedicated to the healthy domain and the six others to each inverter power switch. The fuzzy bases of the proposed technique are extracted from the current analysis of the fault modes in the PWM-VSI. Experimental results on a 1.5-kW induction motor drive are presented to demonstrate the effectiveness of the proposed fuzzy approach. Index Terms--Concordia transform, diagnosis, fault detection, fuzzy logic, induction motor, voltage source inverter (VSI).

This paper describes active fault-tolerant control systems for a high-performance induction-motor drive that propels an electrical vehicle (EV) or a hybrid one (HEV). The proposed systems adaptively reorganize themselves in the event of sensor loss or sensor recovery to sustain the best control performance, given the complement of remaining sensors. Moreover, the developed systems take into account the controller-transition smoothness, in terms of speed and torque transients. The two proposed fault-tolerant control strategies have been simulated on a 4-kW induction-motor drive, and speed and torque responses have been carried to evaluate the consistency and the performance of the proposed approaches. Simulation results, in terms of speed and torque responses, show the global effectiveness of the proposed approaches, particularly the one based on modern and intelligent control techniques in terms of speed and torque smoothness. Index Terms--Automotive application, fault-tolerant control, induction-motor drive.

This paper proposes a strategy to minimize the losses of an induction motor propelling an electric vehicle (EV). The proposed control strategy, which is based on a direct flux and torque control scheme, utilizes the stator flux as a control variable, and the flux level is selected in accordance with the torque demand of the EV to achieve the efficiency-optimized drive performance. Moreover, among EV's motor electric propulsion features, the energy efficiency is a basic characteristic that is influenced by vehicle dynamics and system architecture. For this reason, the EV dynamics are taken into account. Simulation tests have been carried out on a 1.1-kW EV induction motor drive to evaluate the consistency and the performance of the proposed control approach. Index Terms--Direct torque control (DTC), electric vehicle (EV), induction motor, loss minimization.

This paper describes a comparative study allowing the selection of the most appropriate electric-propulsion system for a parallel hybrid electric vehicle (HEV). This paper is based on an exhaustive review of the state of the art and on an effective comparison of the performances of the four main electric-propulsion systems, namely the dc motor, the induction motor (IM), the permanent magnet synchronous motor, and the switched reluctance motor. The main conclusion drawn by the proposed comparative study is that it is the cage IM that better fulfills the major requirements of the HEV electric propulsion. Index Terms--Comparison, electric propulsion, hybrid electric vehicle (HEV).

The aim of this paper is to study the feasibility of fault detection and diagnosis in a three-phase inverter feeding an induction motor. The proposed approach is a sensor-based technique using the mains current measurement. A localization domain made with seven patterns is built with the stator Concordia mean current vector. One is dedicated to the healthy domain and the last six are to each inverter switch. A probabilistic approach for the definition of the boundaries increases the robustness of the method against the uncertainties due to measurements and to the PWM. In high-power equipment where it is crucial to detect and diagnose the inverter faulty switch, a simple algorithm compares the patterns and generates a Boolean indicating the faulty device. In low-power applications (less than 1 kW) where only fault detection is required, a radial basis function (RBF) evolving architecture neural network is used to build the healthy operation area. Simulated experimental results on 0.3- and 1.5-kW induction motor drives show the feasibility of the proposed approach. Index Terms--Concordia transform, fault detection and diagnosis, induction motor, inverter, pattern recognition.

Recently, the permanent-magnet synchronous motor (PMSM) has found widespread utilization in modern adjustable ac drives. This is achieved by using current-controlled voltage source inverter (VSI) systems. Because of its ease implementation, fast current control response and inherent peak current-limiting capability, hysteresis current control is considered as the simplest technique used to control the motor currents for an ac machine. On the other hand, the ramp comparator controller has some advantages, such as limiting maximum inverter switching frequency to the carrier triangular waveform frequency and producing well-defined harmonics. In order to take advantage of the position features of both these two controllers, this paper presents the design and software implementation of a hybrid current controller. The proposed intelligent controller is a simultaneous combination and contribution of the hysteresis current controller and the ramp comparator. Comparisons using simulations on a 0.9-kW PMSM confirm that the proposed hybrid current controller gives better performance and has the advantage of conceptual simplicity. In particular, harmonic spectra of the stator current, obtained using a fast Fourier transform (FFT), are used for comparison purposes. Index Terms--Hybrid controller, hysteresis controller, permanent-magnet synchronous motor, ramp comparator, robustness.

In recent years, marked improvement has been achieved in the design and manufacture of stator winding. However, motors driven by solid-state inverters undergo severe voltage stresses due to rapid switch-on and switch-off of semiconductor switches. Also, induction motors are required to operate in highly corrosive and dusty environments. Requirements such as these have spurred the development of vastly improved insulation material and treatment processes. But cage rotor design has undergone little change. As a result, rotor failures now account for a larger percentage of total induction motor failures. Broken cage bars and bearing deterioration are now the main cause of rotor failures. Moreover, with advances in digital technology over the last years, adequate data processing capability is now available on cost-effective hardware platforms, to monitor motors for a variety of abnormalities on a real time basis in addition to the normal motor protection functions. Such multifunction monitors are now starting to displace the multiplicity of electromechanical devices commonly applied for many years. For such reasons, this paper is devoted to a comparison of signal processing-based techniques for the detection of broken bars and bearing deterioration in induction motors. Features of these techniques which are relevant to fault detection are presented. These features are then analyzed and compared to deduce the most appropriate technique for induction motor rotor fault detection. Index Terms--Induction motor, rotor fault diagnosis, stator current.

The reliability of power electronics systems is of paramount importance in industrial, commercial, aerospace, and military applications. The knowledge about fault mode behavior of an induction motor drive system is extremely important from the standpoint of improved system design, protection, and fault-tolerant control. This paper addresses the application of motor current spectral analysis for the detection and localization of abnormal electrical and mechanical conditions that indicate, or may lead to, a failure of induction motors. Intensive research effort has been for some time focused on the motor current signature analysis. This technique utilizes the results of spectral analysis of the stator current. Reliable interpretation of the spectra is difficult since distortions of the current waveform caused by the abnormalities in the induction motor are usually minute. This paper takes the initial step to investigate the efficiency of current monitoring for diagnostic purposes. The effects of stator current spectrum are described and the related frequencies determined. In the present investigation, the frequency signature of some asymmetrical motor faults are well identified using advanced signal processing techniques, such as high-resolution spectral analysis. This technique leads to a better interpretation of the motor current spectra. In fact, experimental results clearly illustrate that stator current high-resolution spectral analysis is very sensitive to induction motor faults modifying main spectral components, such as voltage unbalance and single-phasing effects. Index Terms - Broken bars, damaged bearings, fault detection, induction motors, rotor eccentricity, shaft speed oscillation, single-phasing effects, spectral analysis, stator current, unbalanced voltage.

Finite element modelling of giant magnetostrictive materials (Terfenol-D rods, Tb0.3 Dy0.7 Fe2), is an interesting but difficult task. Not only the coupling between electromagnetic and mechanical phenomena is essential, it is also a full magnetomechanical nonlinear problem. Confronted with the need to come closer to the reality, nonlinear modelling is necessary. For this purpose a variational formulation is proposed to analyze, by using the Finite Element Method (FEM), the giant magnetostriction phenomena, particularly the Joule effect in the magnetostatic case. It is proved, using Euler's and Neumann's theorems, that the proposed model corresponds to the mathematical setting of the magnetomechanical coupled problem. It should be noted that the model is strongly based on static registrations of the magnetic and mechanical properties which are characteristically complex and nonlinear. A solution is then suggested to fit this type of experimental data through the thin sheets bending principle which is more efficient when used for finite element modelling.

This paper is intended as a tutorial overview of induction motors signature analysis as a medium for fault detection. The purpose is to introduce in a concise manner the fundamental theory, main results, and practical applications of motor signature analysis for the detection and the localization of abnormal electrical and mechanical conditions that indicate, or may lead to, a failure of induction motors. The paper is focused on the so-called motor current signature analysis which utilizes the results of spectral analysis of the stator current. The paper is purposefully written without 'state-of-the-art' terminology for the benefit of practicing engineers in facilities today who may not be familiar with signal processing. Index Terms--Fault detection, induction motor, motor current signature analysis.

Various applications of artificial neural networks (ANN's) presented in the literature prove that such technique is well suited to cope with online faults diagnosis in induction motors. The aim of this paper is to present a methodology by which induction motors electrical faults can be diagnosed. The proposed methodology is based on the so-called Park's vector approach. In fact, stator current Park's vector patterns are first learned, using ANN's, and then used to discern between 'healthy' and 'faulty' induction motors. The diagnosis process was tested on both classical and decentralized approaches. The purpose of a decentralized architecture is to facilitate a satisfactory distributed implementation of new types of faults to the initial NN monitoring system. The generality of the proposed methodology has been experimentally tested on a 4-kW squirrel-cage induction motor. The obtained results provide a satisfactory level of accuracy, indicating a promising industrial application of the hybrid Park's vector-neural networks approach. Index Terms--Artificial neural networks, diagnosis, induction motor, open phase, Park's vector approach, voltage unbalance.

This paper deals with the broken bars detection in induction motors. The hypothesis on which detection is based is that the apparent rotor resistance of an induction motor will increase when a rotor bar breaks. To detect broken bars, measurements of stator voltages and currents are processed by an extended Kalman filter for the speed and rotor resistance simultaneous estimation. In particular, rotor resistance is estimated and compared with its nominal value to detect broken bars. In the proposed extended Kalman Filter approach, the state covariance matrix is adequacy weighted leading to a better states estimation dynamic. Its main advantage is the correct rotor resistance estimation even for an unloaded induction motor. As part of this estimation process, it is necessary to compensate for the thermal variation in the rotor resistance. Computer simulations, carried out for a 4-kW four-pole squirrel cage induction motor, provide an encouraging validation of the proposed sensorless broken bars detection technique. Index Terms--Broken bars, extended Kalman filter, induction motor, rotor resistance, simultaneous estimation.

In this paper, an effective on-line method for induction motor parameter identification, especially rotor parameters, based on the H-G diagram is presented for motor thermal monitoring purpose. The H-G diagram is established from the analysis of the induction motor measurement of active and reactive power consumption for each operating point. Computer simulations and experimental tests, carried out for a 4 kW four-pole squirrel cage induction motor, provide an encouraging validation of the proposed thermal monitoring technique. The process should be refined for a possible industrial application. Index Terms--H-G diagram, identification, induction motor, rotor parameters, thermal monitoring.

This paper focuses on rolling elements bearing fault detection in induction machines based on stator currents analysis. Specifically, it proposes to process the stator currents using the Hilbert–Huang transform. This approach relies on two steps: empirical mode decomposition and Hilbert transform. The empirical mode decomposition is used in order to estimate the intrinsic mode functions (IMFs). These IMFs are assumed to be mono-component signals and can be processed using demodulation technique. Afterward, the Hilbert transform is used to compute the instantaneous amplitude (IA) and instantaneous frequency (IF) of these IMFs. The analysis of the IA and IF allows identifying fault signature that can be used for more accurate diagnosis. The proposed approach is used for bearing fault detection in induction machines at several fault degrees. The effectiveness of the proposed approach is verified by a series of simulation and experimental tests corresponding to different bearing fault conditions. The fault severity is assessed based on the IMFs energy and the variance of the IA and IF of each IMF. [ABSTRACT FROM PUBLISHER]

This paper deals with power control of a grid connected variable speed wind turbine system, based on doubly fed induction generator. In this paper we propose a novel control scheme of the rotor side converter, that is based on indirect control of power consist currents and powers control loops. The two loops of rotor currents: direct and quadrature component are controlled by the technique of the second order sliding mode using the super-twisting algorithm in order to eliminate the chattering phenomenon. A fuzzy logic controller is applied to the active and to the reactive powers loops exchanged between the stator of the doubly fed induction generator and the grid to track the maximum power point. In order to maintain the voltage of the DC-link constant of the grid side converter, the classical proportional integral controller is used. The proposed control strategy, which combines the second order sliding mode and fuzzy logic, is applied to a 1.5 MW three blade wind turbine shows robustness for variations in the wind speed. [ABSTRACT FROM AUTHOR]

This paper deals with the diagnosis of three-phase electrical machines and focuses on failures that lead to stator-current modulation. To detect a failure, we propose a new method based on stator-current demodulation. By exploiting the configuration of three-phase machines, we demonstrate that the demodulation can be efficiently performed with low-complexity multidimensional transforms such as the Concordia transform (CT) or the principal component analysis (PCA). From a practical point of view, we also prove that PCA-based demodulation is more attractive than CT. After demodulation, we propose two statistical criteria aiming at measuring the failure severity from the demodulated signals. Simulations and experimental results highlight the good performance of the proposed approach for condition monitoring. [ABSTRACT FROM AUTHOR]

Earlier studies have pointed out the limitations of conventional inverters, especially in high-voltage and high-power applications. In recent years, multilevel inverters are becoming increasingly popular for high-power applications due to their improved harmonic profile and increased power ratings. Several studies have been reported in the literature on multilevel inverters topologies, control techniques, and applications. However, there are few studies that actually discuss or evaluate the performance of induction motor drives associated with three-phase multilevel inverter. This paper presents then a comparison study for a cascaded H-bridge multilevel direct torque control (DTC) induction motor drive. In this case, symmetrical and asymmetrical arrangements of five- and seven-level H-bridge inverters are compared in order to find an optimum arrangement with lower switching losses and optimized output voltage quality. The carried out experiments show that an asymmetrical configuration provides nearly sinusoidal voltages with very low distortion, using less switching devices. Moreover, torque ripples are greatly reduced. [ABSTRACT FROM PUBLISHER]

This paper presents a hybrid cascaded H-bridge multilevel motor drive direct torque control (DTC) scheme for electric vehicles (EVs) or hybrid EVs. The control method is based on DTC operating principles. The stator voltage vector reference is computed from the stator flux and torque errors imposed by the flux and torque controllers. This voltage reference is then generated using a hybrid cascaded H-bridge multilevel inverter, where each phase of the inverter can be implemented using a dc source, which would be available from fuel cells, batteries, or ultracapacitors. This inverter provides nearly sinusoidal voltages with very low distortion, even without filtering, using fewer switching devices. In addition, the multilevel inverter can generate a high and fixed switching frequency output voltage with fewer switching losses, since only the small power cells of the inverter operate at a high switching rate. Therefore, a high performance and also efficient torque and flux controllers are obtained, enabling a DTC solution for multilevel-inverter-powered motor drives. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of detection and diagnosis of induction motor faults. Using the fuzzy logic strategy, a better understanding of heuristics underlying the motor faults detection and diagnosis process can be achieved. The proposed fuzzy approach is based on the stator current Concordia patterns. Induction motor stator currents are measured, recorded, and used for Concordia patterns computation under different operating conditions, particularly for different load levels. Experimental results are presented in terms of accuracy in the detection motor faults and knowledge extraction feasibility. The preliminary results show that the proposed fuzzy approach can be used for accurate stator fault diagnosis if the input data are processed in an advantageous way, which is the case of the Concordia patterns. [ABSTRACT FROM AUTHOR]