Articles de revues sur le sujet « Multi-Three-phase electrical machines »
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Gutierrez-Reina, Daniel, Federico Barrero, Jose Riveros, Ignacio Gonzalez-Prieto, Sergio Toral et Mario Duran. « Interest and Applicability of Meta-Heuristic Algorithms in the Electrical Parameter Identification of Multiphase Machines ». Energies 12, no 2 (19 janvier 2019) : 314. http://dx.doi.org/10.3390/en12020314.
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Multiphase machines are complex multi-variable electro-mechanical systems that are receiving special attention from industry due to their better fault tolerance and power-per-phase splitting characteristics compared with conventional three-phase machines. Their utility and interest are restricted to the definition of high-performance controllers, which strongly depends on the knowledge of the electrical parameters used in the multiphase machine model. This work presents the proof-of-concept of a new method based on particle swarm optimization and standstill time-domain tests. This proposed method is tested to estimate the electrical parameters of a five-phase induction machine. A reduction of the estimation error higher than 2.5% is obtained compared with gradient-based approaches.
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Chandrasekaran, V., et T. Manigandan. « Energy Conservation through Multi Winding Induction Machines ». Advanced Materials Research 433-440 (janvier 2012) : 7165–69. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.7165.
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Three phase induction motors are employed in Textile mills, Agriculture and in almost all the machine tools. More than 60% of electrical energy generated being consumed by the induction motors. Hence, even a small contribution in the improvement of the power factor and efficiency will be cost effective. The power factor and efficiency of an induction motor is based on the shaft load and in order to improve the same, multi windings are suggested in the same stator. In multi winding induction machines, when one set of windings is connected to a three phase a supply, a revolving magnetic field of constant magnitude is developed in the air gap which is responsible to work as a conventional induction motor to meet the mechanical load and to develop a three phase EMF in the other winding that works as an Induction Alternator (IA).Double Winding Induction Motor (DWIM) also provides an opportunity to load each winding individually to its rated capacity. A small three phase load or a single phase load may be connected to the second set of winding. The dependency of separate supply for this load is eliminated. Hence, improvement in the efficiency, power factor and energy conservation is made possible in these machines. In order to validate the problem statement, a 3-phase, 3.0 kW, 415 V Double Winding Induction Motor(DWIM), a 3-phase, 3.0 kW, 415 V Double Winding Synchronous Reluctance Motor (DWSyRM), a 3-phase, 2.2 kW, 415 V Three Winding Induction Motor (TWIM) have been designed, fabricated and tested. Two controllers have been designed, one for a DWIM to operate the motor in power balancing and maximum efficiency modes of operation and other to operate TWIM at three different voltage levels depending upon the shaft load. In this paper, detailed comparisons of performances of three multi winding machines are presented.
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Furtat, Igor, Pavel Gushchin, Evgeny Tupichin et Alexey Peregudin. « Control study of multi-machine power systems under variations of mechanical input power and communication delay ». Cybernetics and Physics, Volume 8, 2019, Number 4 (30 décembre 2019) : 235–43. http://dx.doi.org/10.35470/2226-4116-2019-8-4-235-243.
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The paper studies the robust control for multi-machine power systems under parametric uncertainties, perturbed mechanical input power and unknown variable communication time-delay. Only relative speed of each electrical generator is available for measurement. The theoretical investigations show that the proposed algorithm synchronizes the multi-machine power system with the required accuracy in the normal mode and under symmetrical 3-phase short circuit faults which occur on ransmission lines. Numerical investigations illustrate the efficiency of the proposed scheme for the three machines and the New England Power system benchmark.
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Wang, Xuchen, Giacomo Sala, He Zhang, Chunyang Gu, Giampaolo Buticchi, Andrea Formentini, Chris Gerada et Patrick Wheeler. « Torque Ripple Reduction in Sectored Multi Three-Phase Machines Based on PWM Carrier Phase Shift ». IEEE Transactions on Industrial Electronics 67, no 6 (juin 2020) : 4315–25. http://dx.doi.org/10.1109/tie.2019.2931239.
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Sala, Giacomo, Michele Mengoni, Gabriele Rizzoli, Luca Zarri et Angelo Tani. « Decoupled d–q Axes Current-Sharing Control of Multi-Three-Phase Induction Machines ». IEEE Transactions on Industrial Electronics 67, no 9 (septembre 2020) : 7124–34. http://dx.doi.org/10.1109/tie.2019.2941127.
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Barre, Olivier, et Bellemain Napame. « Fractional Slot Concentrated Windings : A New Method to Manage the Mutual Inductance between Phases in Three-Phase Electrical Machines and Multi-Star Electrical Machines ». Machines 3, no 2 (10 juin 2015) : 123–37. http://dx.doi.org/10.3390/machines3020123.
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Gong, Jinlin, Benteng Zhao, Youxi Huang, Eric Semail et Ngac Ky Nguyen. « Quantitative Comparisons of Outer-Rotor Permanent Magnet Machines of Different Structures/Phases for In-Wheel Electrical Vehicle Application ». Energies 15, no 18 (13 septembre 2022) : 6688. http://dx.doi.org/10.3390/en15186688.
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As one of the key components, low-speed direct-drive in-wheel machines with high compact volume and high torque density are important for the traction system of electric vehicles (EVs). This paper introduces four different types of outer-rotor permanent magnet motors for EVs, including one five-phase SPM machine, one three-phase IPM machine with V-shaped PMs, one seven-phase axial flux machine (AFM) of sandwich structure and finally one hybrid flux (radial and axial) machine with a third rotor with V-shaped PMs added to the AFM. Firstly, the design criteria and basic operation principle are compared and discussed. Then, the key properties are analyzed using the Finite Element Method (FEM). The electromagnetic properties of the four fractional slot tooth concentrated winding in-wheel motors with similar dimensions are quantitatively compared, including air-gap flux density, electromotive force, field weakening capability, torque density, losses, and fault tolerant capability. The results show that the multi-phase motors have high torque density and high fault tolerance and are suitable for direct drive applications in EVs.
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Jedryczka, Cezary. « Comparative analysis of the three- and six-phase fractional slot concentrated winding permanent magnet machines ». COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 36, no 3 (2 mai 2017) : 811–23. http://dx.doi.org/10.1108/compel-09-2016-0431.
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Purpose The purpose of this paper is to analyse and compare the functional parameters of three- and six-phase permanent magnet synchronous motors (PMSM) with fractional-slot concentrated windings (FSCW). Design/methodology/approach The investigations are focused on the comparison of the distortions of back electromotive force (emf) and magnetomotive force (mmf) waveforms, as well as torque ripples, radial force spatial harmonics and motor performance studies. The finite element models of the test machine and a personally developed computer code have been used to calculate motor characteristics and analyse and synthesise multiphase winding layouts, respectively. Findings Compared with the traditional three-phase PMSM designs, the proposed six-phase machines are characterized by a significantly lower content of sub-harmonics in mmf waveform distribution. Moreover, the investigated six-phase machines exhibited a higher average value of electromagnetic torque, significantly lower torque ripples and a reduced value of low-order harmonics of the radial component of the electromagnetic force in the air-gap of the machine. Originality/value The analyses presented in this paper show that six-phase PMSM with FSCWs are advantageous to their counterpart three-phase machines. Specifically, they are more suited to working with multiple drives supplying a segmented winding system while simultaneously offering higher performance. This suitability to the use of a multi-drive supply for one motor offers flexibility and cost reduction while increasing the fault tolerance of a power train system.
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Babu, Yaramasu Suri, et Koritala Chandra Sekhar. « Five-phase induction motor drive for electric vehicle with high gain switched-inductor quasi impedance source inverter ». International Journal of Power Electronics and Drive Systems (IJPEDS) 13, no 1 (1 mars 2022) : 411. http://dx.doi.org/10.11591/ijpeds.v13.i1.pp411-422.
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Switched-inductor quasi-impedance source inverter (SL-qZSI) with high gain fed five-phase induction motor drive for electric vehicle (EV) applications is proposed in this paper. Multi-phase electric machines have been predicted for application where the entire system should have better reliability and demanded power per phase is low especially for autonomous applications like EVs. To supply variable voltage and frequency to multi-phase machines, multi-phase inverters are required. SL-qZSI offers enhanced boost capability with respect to traditional impedance source converters by inserting three diodes and only one inductor to the basic quasi-impedance source inverter (qZSI). Also, SL-qZSI offers withstanding capability during voltage dip results line harmonics are diminished; enhances reliability of inverter; and extended output voltage range. The key idea of this paper is to design and develop a high performance and highly reliable SL-qZSI fed five-phase Induction Motor drive and validate the proposed system depends on results of Simulation with the help of MATLAB; these findings were comparable to the similar type of existing converters. We can notice from the performance analysis of the proposed system that it can provide enhanced voltage boosting capability and proved that it has significant potential for the suggested multi-phase variable speed drive (VSD) system.
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Zhang, Jian-Ya, Qiang Zhou et Kai Wang. « Dual Three-Phase Permanent Magnet Synchronous Machines Vector Control Based on Triple Rotating Reference Frame ». Energies 15, no 19 (4 octobre 2022) : 7286. http://dx.doi.org/10.3390/en15197286.
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This paper presents a triple rotating coordinate transformed vector control method for dual three-phase permanent magnet (PM) machines. In the proposed scheme, the control variables are converted to three sets of αβ components directly, which are 120° electric degrees different from each other. It omits the complicated six-dimensional transformed matrix and reduces the computation greatly. The relationship with vector space (VSD) control was mathematically analyzed. By ensuring the consistency of control variables in the three stationary reference frames, the suggested method can not only achieve the same fundamental control performance as VSD but compensate for the imbalance current caused by the harmonics in the back electromotive force. In addition, the proposed method belongs to multi redundancy control in theory, which is maybe a good solution for fault-tolerant operation. Finally, a prototype dual three-phase PM machine was tested. The experimental results are in good agreement with the theoretical analysis.
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Mustafa, Uvais, Rishad Ahmed, Alan Watson, Patrick Wheeler, Naseer Ahmed et Parmjeet Dahele. « A Comprehensive Review of Machine-Integrated Electric Vehicle Chargers ». Energies 16, no 1 (22 décembre 2022) : 129. http://dx.doi.org/10.3390/en16010129.
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Electric Vehicles are becoming increasingly popular due to their environment friendly operation. As the demand for electric vehicles increases, it has become quite important to explore their charging strategies. Since charging and traction do not normally occur simultaneously and the power electronics converters for both operations have some similarities, the practice of integrating both charging and traction systems is becoming popular. These types of chargers are termed ‘Integrated Chargers’. The aim of this paper is to review the available literature on the integrated chargers and present a critical analysis of the pros and cons of different integrated charging architectures. Integrated chargers for electric vehicles with three-phase permanent magnet synchronous machines, multi-phase machines and switched reluctance machines were compared. The challenges with the published integrated chargers and the future aspect of the work were been discussed.
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Jedryczka, Cezary, Wojciech Szelag et Zbigniew Jerry Piech. « Multiphase permanent magnet synchronous motors with fractional slot windings ». COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 35, no 6 (7 novembre 2016) : 1937–48. http://dx.doi.org/10.1108/compel-03-2016-0120.
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Purpose The purpose of this paper is to investigate advantages of multiphase permanent magnet synchronous motors (PMSM) with fractional slot concentrated windings (FSCW). The investigation is based on comparative analysis and assessment of FSCW PMSM wound as 3, 6, 9 and 12 phase machines suited for low speed applications. Design/methodology/approach The investigations are focussed on distortions of back electromotive (emf) and magnetomotive force (mmf) with the torque ripples and motors’ performance taken into account. The finite element models with the aid of customized computer code have been adopted for motor winding design and back emf, mmf and motor performance analyses. Findings The novel multiphase winding layouts were found to offer lower content of sub-harmonics in the mmf waveforms compared with the traditional three-phase machine designs. Moreover, the investigated multiphase machines exhibited higher average value of the electromagnetic torque, while the multiphase PMSM machines with FSCW were further characterized by significantly lower torque pulsations. Originality/value The analyses presented in this paper demonstrate that PMSM with FSCW are advantageous to their counterpart three-phase machines. Specifically, they offer higher performance and are more suitable to work with multiple drives supplying segmented winding system. This ability of using multi-drive supply for one motor offers flexibility and cost reduction while increasing fault tolerant power train system.
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Rezaoui, Mohamed Mounir, Imad Merzouk, et Noureddine Bessous. « Control of 3x7 matrix converter with PWM three intervals modulation ». International Journal of Energetica 4, no 2 (1 janvier 2020) : 14. http://dx.doi.org/10.47238/ijeca.v4i2.103.
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Direct Power conversion from fixed AC voltage into variable AC voltage is gaining a significant attention, especially in case of multi-phases machines/generators; for such reason a new algorithm to control 3x7 matrix converter (MC) is developed in this paper, wherein the main aim is to control multi-phases induction motor/generator connected to the electrical grid with a novel converter (except matrix converter), for that the PWM three intervals modulation strategy is modified from the control of 3x3 MC to 3x7 MC; which is directly connected to the network through a three phase input in order to overcome the supplying problems, on the other side seven phases have been used as an output to benefit the advantages of the multi-phases machines. This paper intends in the first place to explain the 3x3 MC, then to manipulate the control equation for the purpose of making it suitable for controlling the 3x7 MC effectively, thus a good performance can be clearly seen according to the quality of the output voltage/current under typical R-L load, the shift between phases and the THD evaluation. The obtained simulation results which demonstrate the efficiency of the new control algorithm are presented and discussed.
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Drozdowski, Piotr, et Dariusz Cholewa. « Voltage Control of Multiphase Cage Induction Generators at a Speed Varying over a Wide Range ». Energies 14, no 21 (29 octobre 2021) : 7080. http://dx.doi.org/10.3390/en14217080.
Texte intégralRésumé :
The subject of this publication is a method of controlling the DC voltage of a PWM rectifier supplied by a multiphase cage induction generator with the number of stator phases greater than three operating in a wide range of driving speeds. Voltage regulation is performed by changing the frequency and amplitude of the stator voltages with simultaneous switching of the phase sequence of these voltages. The step change of the voltage sequence is made in the designated ranges of the generator speed, which enables the stabilization of the output voltage in a wide range from the minimum speed of about 25% of the rated speed. Such sequence switching changes the number of pole pairs produced by the winding for each supply sequence. The difference compared to multi-speed induction machines is that, in the presented solution, there is only one winding, not a few, which enables good use of the machine’s magnetic core in the same dimensions as for the three-phase machine of a similar power. Steady-state characteristics and dynamic operation were obtained using laboratory measurements of a standalone nine-phase induction generator. The automatic control system maintained the output voltage at the set level, regardless of the generator load and driving power.
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Garbiec, Tomasz, et Mariusz Jagiela. « Accounting for Slot Harmonics and Nonsinusoidal Unbalanced Voltage Supply in High-Speed Solid-Rotor Induction Motor Using Complex Multi-Harmonic Finite Element Analysis ». Energies 14, no 17 (30 août 2021) : 5404. http://dx.doi.org/10.3390/en14175404.
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Solid rotor induction machines are still used in high-speed systems. A two-dimensional field-circuit model based on the finite element method and the complex magnetic vector potential has been shown as a very time-effective tool in the analysis of their steady states compared to time-domain models. This continuation work presents a validated computational algorithm that enables the inclusion of the nonsinusoidal and/or asymmetrical voltage supply in the multi-harmonic field-circuit model of these machines that was presented in the previous works by the authors. The extended model accounts for both spatial harmonics due to slotting and/or winding distribution and the time-harmonics due to voltage waveform. The applicability range of the model therefore increases to cases when the machine is supplied with a nonsinusoidal three-phase system of voltages with symmetry or asymmetry that can be decomposed into three symmetrical components. Its short execution time characteristic allows for much more insightful design studies of the contribution of voltage supply- and slotting-related harmonics to the overall efficiency of the machine than is possible with the time-consuming time-domain models. The proposed computational framework has never been presented in the literature. The model is verified positively by the comprehensive time-domain model. It is especially useful in design studies on solid rotor induction motors related to the optimisation of the efficiency of induction motor-based drive systems.
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Mongay Batalla, Jordi, Constandinos X. Mavromoustakis, George Mastorakis, Evangelos K. Markakis, Evangelos Pallis, Tomasz Wichary, Piotr Krawiec et Przemysław Lekston. « On Analyzing Routing Selection for Aerial Autonomous Vehicles Connected to Mobile Network ». Sensors 21, no 2 (8 janvier 2021) : 399. http://dx.doi.org/10.3390/s21020399.
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This paper proposes a two-phase algorithm for multi-criteria selection of packet forwarding in unmanned aerial vehicles (UAV), which communicate with the control station through commercial mobile network. The selection of proper data forwarding in the two radio link: From UAV to the antenna and from the antenna to the control station, are independent but subject to constrains. The proposed approach is independent of the intra-domain forwarding, so it may be useful for a number of different scenarios of Unmanned Aerial Vehicles connectivity (e.g., a swarm of drones). In the implementation developed in this paper, the connection is served by three different mobile network operators in order to ensure reliable connectivity. The proposed algorithm makes use of Machine Learning tools that are properly trained for predicting the behavior of the link connectivity during the flight duration. The results presented in the last section validate the algorithm and the training process of the machines.
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Mongay Batalla, Jordi, Constandinos X. Mavromoustakis, George Mastorakis, Evangelos K. Markakis, Evangelos Pallis, Tomasz Wichary, Piotr Krawiec et Przemysław Lekston. « On Analyzing Routing Selection for Aerial Autonomous Vehicles Connected to Mobile Network ». Sensors 21, no 2 (8 janvier 2021) : 399. http://dx.doi.org/10.3390/s21020399.
Texte intégralRésumé :
This paper proposes a two-phase algorithm for multi-criteria selection of packet forwarding in unmanned aerial vehicles (UAV), which communicate with the control station through commercial mobile network. The selection of proper data forwarding in the two radio link: From UAV to the antenna and from the antenna to the control station, are independent but subject to constrains. The proposed approach is independent of the intra-domain forwarding, so it may be useful for a number of different scenarios of Unmanned Aerial Vehicles connectivity (e.g., a swarm of drones). In the implementation developed in this paper, the connection is served by three different mobile network operators in order to ensure reliable connectivity. The proposed algorithm makes use of Machine Learning tools that are properly trained for predicting the behavior of the link connectivity during the flight duration. The results presented in the last section validate the algorithm and the training process of the machines.
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Wogi, Lelisa, Tadele Ayana, Marcin Morawiec et Andrzej Jąderko. « A Comparative Study of Fuzzy SMC with Adaptive Fuzzy PID for Sensorless Speed Control of Six-Phase Induction Motor ». Energies 15, no 21 (2 novembre 2022) : 8183. http://dx.doi.org/10.3390/en15218183.
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Multi-phase motors have recently replaced three-phase induction motors in a variety of applications due to the numerous benefits they provide, and the absence of speed sensors promotes induction motors with variable speed drives. Sensorless speed control minimizes unnecessary speed encoder cost, reduces maintenance, and improves the motor drive’s reliability. The performance comparison of the fuzzy sliding mode controller (FSMC) with adaptive fuzzy proportional integral derivative (AFPID) control methods for sensorless speed control of six-phase induction motors was analyzed in this study, and the proposed control system has an advantage for multiphase machines, specifically six-phase induction motors (IMs) in this study, as they are the current active research area for electric vehicles, hybrid electric vehicles, aerospace, ship propulsion, and high-power applications. The speed control of a six-phase induction motor was performed by using an AFPID controller and FSMC. The comparative performance analysis was based on sensorless speed control of the six-phase induction motor. A proportional integral derivative (PID) controller is commonly employed as it is used to eliminate oscillations, but it has several drawbacks, such as taking a long time to decrease the error and stabilize the system at constant speed. The fuzzy type-2 and PID controllers were hybridized so as to obtain the advantages of both to enhance the system performance. Finally, the comparison result revealed that the FSMC preforms significantly better by achieving good tracking performance. The control technique maintains the sliding mode approach’s robustness while providing reduced overshoots with a smooth control action, and the FSMC revealed good dynamic response under load variations when compared to the AFPID controller.
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Chavan, Gayatri, et Sridhar S. « Speed Control of Dual Induction Motor Using Five Leg Inverter ». E3S Web of Conferences 184 (2020) : 01065. http://dx.doi.org/10.1051/e3sconf/202018401065.
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Over the past decade, research efforts have been made to decrease the number of power electronic devices needed in multi-motor drive systems in order to condense the overall complication and cost of the drive. This paper proposes speed control for a dual three-phase induction motor system driven by five-leg voltage source inverter (FL-VSI) which is used in industrial manufacturing processes. Industrial applications frequently need a number of variable speed electric drives. This technique uses five-leg inverter instead of using conventional two three phase inverters. In the majority of cases, these multi-motor drive systems need independent control of individual motors. It is shown recently that it is possible to separately control two three-phase induction machines supplied through a five-leg voltage source inverter, with one inverter leg being common to both machines. The entire performance of the speed control for the five leg voltage source inverter fed dual-motor drive system is investigated using MATLAB/SIMULINK software.
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CHARRAK, Naas, Abdallah KOUZOU, Belgacem Said KHAlDI et Lazhri NEZLI. « Analysis of Multi-Phase qZ-Source Inverter with Maximum Constant Boost Control Technique ». Electrotehnica, Electronica, Automatica 69, no 1 (15 février 2021) : 37–43. http://dx.doi.org/10.46904/eea.21.69.1.1108005.
Texte intégralRésumé :
This paper proposes a topology of two-level qZ- source multiphase inverter based on maximum constant boost control algorithm which can be used to power multiphase machines. Indeed, these kinds of machines have many advantages compared to their counterpart three-phase machines, which are actually used extensively in many industries. On the other side, the qZ-source inverter has also many advantages compared to the conventional inverters, mainly high boosting capability, more freedom degrees of switching states and less voltage and current stresses of switches can be obtained. Such advantages among others make it to be a good candidate in many industrial applications. Thus, the present paper proposes the topology of two level five-phase qZ-source inverter, where the main aim is to be used for driving the five-phase machine and consequently benefiting from the merits of the advantages of both topologies of the machine and the inverter in building one combined topology. The simulations results obtained in this paper prove the quality of the improved performances of the proposed topology in comparison with previous works which can be a promising solution in many industrial applications based on the afore mentioned advantages.
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Golubev, A. N., et V. G. Belonogov. « The development of multi-channel control system for a multi-phase synchronous electric drive with improved vibration noise characteristics ». Vestnik IGEU, no 3 (30 juin 2020) : 43–50. http://dx.doi.org/10.17588/2072-2672.2020.3.043-050.
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Constantly increasing requirements for the performance of electromechanical systems include the task of improving the vibration and noise characteristics of the electric drive. Currently, this problem is solved mainly by using traditional three-phase systems. The transition to a multiphase version of the electric drive construction opens up new opportunities for its solution. The aim of the study is to improve the vibration and noise characteristics of the electric drive through the development of a multiphase control system that provides targeted formation of the field configuration in the gap of a multiphase electric machine. To conduct the research, the model of a multiphase synchronous motor proposed by the authors was used. The model considers the spatial non-sinusoidality of the field distribution in the gap and enables to represent the engine as a set of parallel substructures, the number of which depends on the number of phases. The technique to design a synchronous motor with an arbitrary number of winding phases based on the field model of the machine has been proposed. The correctness of the results obtained is ensured by the real geometry of the magnetic circuit and steel saturation. The multi-channel control system of the electric drive, characterized by the targeted formation of field configuration in the gap of a multiphase electric machine has been proposed. The calculation model of the electric drive has been developed. The model combines the field model of the engine and the control system. The engine model is implemented in the ElСut software package, the control system model is implemented in the MatLab (Simulink) complex. Compared to the traditional three-phase design of the electric drive with a sinusoidal supply voltage, the options for forming a field in the gap of a synchronous motor considered in the article provide a reduction in ponderomotive force by 8–14 %. The engineering methodology for designing an m-phase synchronous permanent magnet motor with permanent magnets and the structure of the multi-channel control system can be applied when developing electric drives with improved vibration and noise characteristics.
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Bensaoucha, Saddam, Youcef Brik, Sandrine Moreau, Sid Ahmed Bessedik et Aissa Ameur. « Induction machine stator short-circuit fault detection using support vector machine ». COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 40, no 3 (21 mai 2021) : 373–89. http://dx.doi.org/10.1108/compel-06-2020-0208.
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Purpose This paper provides an effective study to detect and locate the inter-turn short-circuit faults (ITSC) in a three-phase induction motor (IM) using the support vector machine (SVM). The characteristics extracted from the analysis of the phase shifts between the stator currents and their corresponding voltages are used as inputs to train the SVM. The latter automatically decides on the IM state, either a healthy motor or a short-circuit fault on one of its three phases. Design/methodology/approach To evaluate the performance of the SVM, three supervised algorithms of machine learning, namely, multi-layer perceptron neural networks (MLPNNs), radial basis function neural networks (RBFNNs) and extreme learning machine (ELM) are used along with the SVM in this study. Thus, all classifiers (SVM, MLPNN, RBFNN and ELM) are tested and the results are compared with the same data set. Findings The obtained results showed that the SVM outperforms MLPNN, RBFNNs and ELM to diagnose the health status of the IM. Especially, this technique (SVM) provides an excellent performance because it is able to detect a fault of two short-circuited turns (early detection) when the IM is operating under a low load. Originality/value The original of this work is to use the SVM algorithm based on the phase shift between the stator currents and their voltages as inputs to detect and locate the ITSC fault.
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Salim, Nur Ashida, Nur Diyana Shahirah Mohd Zain, Hasmaini Mohamad, Zuhaila Mat Yasin et Nur Fadilah Ab Aziz. « Multi-machine transient stability by using static synchronous series compensator ». International Journal of Power Electronics and Drive Systems (IJPEDS) 11, no 3 (1 septembre 2020) : 1249. http://dx.doi.org/10.11591/ijpeds.v11.i3.pp1249-1258.
Texte intégralRésumé :
<span lang="EN-US">Transient stability in power system is vital to be addressed due to large disturbances that could damage the system such as load changes and voltage increases. This paper presents a multi-machine transient stability using the Static Synchronous Series Compensator (SSSC). SSSC is a device that is connected in series with the power transmission line and produces controllable voltage which contribute to a better performance in the power system stability. As a result, this research has observed a comparison of the synchronization of a three-phase system during single-phase faults before and after installing the SSSC device. In addition, this research investigates the ability of three different types of controllers i.e. Proportional Integral (PI), Proportional Integral Derivation (PID), and Generic controllers to be added to the SSSC improve the transient stability as it cannot operate by itself. This is because the improvement is too small and not able to achieve the desired output. The task presented is to improve the synchronization of the system and time taken for the voltage to stabilize due to the fault. The simulation result shows that the SSSC with an additional controller can improve the stability of a multi-machine power system in a single phase fault.</span>
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Bazan, Gustavo Henrique, Alessandro Goedtel, Oscar Duque-Perez et Daniel Morinigo-Sotelo. « Multi-Fault Diagnosis in Three-Phase Induction Motors Using Data Optimization and Machine Learning Techniques ». Electronics 10, no 12 (18 juin 2021) : 1462. http://dx.doi.org/10.3390/electronics10121462.
Texte intégralRésumé :
Induction motors are very robust, with low operating and maintenance costs, and are therefore widely used in industry. They are, however, not fault-free, with bearings and rotor bars accounting for about 50% of the total failures. This work presents a two-stage approach for three-phase induction motors diagnosis based on mutual information measures of the current signals, principal component analysis, and intelligent systems. In a first stage, the fault is identified, and, in a second stage, the severity of the defect is diagnosed. A case study is presented where different severities of bearing wear and bar breakage are analyzed. To test the robustness of the proposed method, voltage imbalances and load torque variations are considered. The results reveal the promising performance of the proposal with overall accuracies above 90% in all cases, and in many scenarios 100% of the cases are correctly classified. This work also evaluates different strategies for extracting the signals, showing the possibility of reducing the amount of information needed. Results show a satisfactory relation between efficiency and computational cost, with decreases in accuracy of less than 4% but reducing the amount of data by more than 90%, facilitating the efficient use of this method in embedded systems.
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Chen, Qixu, Guoli Li, Wenping Cao, Zhe Qian et Qunjing Wang. « Winding MMF and PM MMF Analysis of Axial-Flux Machine with Multi-Phase and Multi-Layer Winding ». Energies 14, no 16 (20 août 2021) : 5147. http://dx.doi.org/10.3390/en14165147.
Texte intégralRésumé :
According to star graph and winding distribution, winding MMFs of three kinds of 12-slot/10-pole multi-phase and multi-layer winding layout are analyzed by the improved winding function method. Analysis results show that multi-phase and multi-layer winding can suppress even order and (12n ± 1) order harmonics, thereby reducing the eddy-current loss in PMs. Based on the unfolded LPMSM model, rotor MMF, air gap flux density, and no-load back-EMF are analyzed by the analytical permeance function method, which is validated by Teslameter and no-load experiment. Winding MMF is validated by FEM. An axial-flux integrated starting/generator (ISG) with double-three-phase four-layer (DTP-FL) winding, segmented armature and PM, and centrifugal fan is designed and manufactured. The no-load and load test with two groups of resistance established has validated the reasonability of the mentioned method. Experiments show that the axial-flux ISG prototype can run at a relative temperature rise.
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Luan, Tianjiao, Zhichao Wang, Yang Long, Zhen Zhang, Qi Li, Zhihao Zhu et Chunhua Liu. « Multi-Virtual-Vector Model Predictive Current Control for Dual Three-Phase PMSM ». Energies 14, no 21 (3 novembre 2021) : 7292. http://dx.doi.org/10.3390/en14217292.
Texte intégralRésumé :
This paper proposes a multi-virtual-vector model predictive control (MPC) for a dual three-phase permanent magnet synchronous machine (DTP-PMSM), which aims to regulate the currents in both fundamental and harmonic subspace. Apart from the fundamental α-β subspace, the harmonic subspace termed x-y is decoupled in multiphase PMSM according to vector space decomposition (VSD). Hence, the regulation of x-y currents is of paramount importance to improve control performance. In order to take into account both fundamental and harmonic subspaces, this paper presents a multi-virtual-vector model predictive control (MVV-MPC) scheme to significantly improve the steady performance without affecting the dynamic response. In this way, virtual vectors are pre-synthesized to eliminate the components in the x-y subspace and then a vector with adjustable phase and amplitude is composed of two effective virtual vectors and a zero vector. As a result, an enhanced current tracking ability is acquired due to the expanded output range of the voltage vector. Lastly, both simulation and experimental results are given to confirm the feasibility of the proposed MVV-MPC for DTP-PMSM.
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Chen, Qixu, Guoli Li, Zhe Qian, Wenping Cao et Qunjing Wang. « Analysis of winding MMF of PMSM with multi-phase and multi-layer layout using holospectrum method ». International Journal of Applied Electromagnetics and Mechanics 68, no 4 (8 avril 2022) : 387–403. http://dx.doi.org/10.3233/jae-210025.
Texte intégralRésumé :
A holographic spectrum method (HSM) of multi-phase and multi-layer winding magnetomotive force (MMF) for 12-slot/10-pole combination with fractional-slot concentrated-winding (FSCW) is proposed in this paper. Taking two kinds of traditional winding layout as example, amplitude and phase distribution of the vth harmonic are calculated. The holospectrum of the multi-phase and multi-layer winding are derived based on three-phase single-layer (TP-SL) and three-phase double-layer (TP-DL) holospectrum theory. Then the finite element method (FEM) is used to validate the resultant winding MMF calculated by the holospectrum method. Results show that two methods have a good consistency in changing tendency. Prototype manufacture and test of axial-flux PMSM with a double-three-phase four-layer (DTP-FL) winding have been completed. A load experiment with two groups of resistance is established to validate its output power of axial-flux permanent magnet synchronous machine (PMSM). The temperature distribution of axial-flux PMSM is evaluated with PT100 and infrared thermal imager. The experiments shows that the axial-flux PMSM runs at a relative temperature rise.
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Panteleev, S. V., A. N. Malashin, D. V. Karkotskiy et Yu V. Suchodolov. « Synthesis of the Algorithm of the Vector Width-Pulse Modulation in a Nine-Phase Active Voltage Rectifier ». ENERGETIKA. Proceedings of CIS higher education institutions and power engineering associations 61, no 4 (20 juillet 2018) : 334–45. http://dx.doi.org/10.21122/1029-7448-2018-61-4-334-345.
Texte intégralRésumé :
The article is devoted to the problems of synthesis of multiphase electromechanic modules consisting of a multiphase electrical machine (m > 3) and a multiphase controlled converter. In the multi-phase design of the electric machine, it is possible to obtain a trapezoidal electromotive force and to increase the specific power conversion provided that the EMF and the current at the output of the m-phase generator are matched. The development of the vector pulse width modulation algorithm for the m-phase active voltage rectifier is considered, which will make it possible to match the shape and phase of the currents and voltages at the output of the m-phase generator in order to obtain the maximum active power of the module under study. The analysis of possible combinations of states of the keys of a nine-phase active rectifier is carried out. Each key state is assigned a base vector in a fixed coordinate system. It is noted that there are a number of combinations in which two or more different key states correspond to one base vector. The system of basic vectors is differentiated into its constituent levels, sectors and subsectors. In order to obtain any given voltage vector that does not coincide with the base vectors, the method of spacevector modulation is used. This method provides using 100 % DC link voltage as compared with sinusoidal pulse width modulation (86.6 %). The goal of space-vector modulation for a nine-phase active voltage rectifier is to implement the resultant spatial vector of the output voltage with a given average value within the modulation period. For this, the three vectors nearest to the given base vector must be found. To implement a given voltage vector, a sequence of sampling the base vectors in the modulation period is given, providing one commutation (commutation of two complementary keys) while passing from one basic vector to another to reduce switching losses. Analytic relationships of the weight coefficients for the generators of vectors are obtained, as well as the equations of the boundaries of the subsectors that form a diagram of combinations of states of the circuit. The sequence of the calculations presented in the paper is a technique for realizing the vector pulse-width modulation in nine-phase controlled electrical energy converters.
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Zhou, Wei Wei, Ji Ye Huang, Ming Yu Gao, Zhi Wei He et Bu Sen Cai. « Design and Realization of CAN-Based Main Control System of Multi-Station Meter Testing Equipment ». Applied Mechanics and Materials 719-720 (janvier 2015) : 411–16. http://dx.doi.org/10.4028/www.scientific.net/amm.719-720.411.
Texte intégralRésumé :
In this paper, 0.05 grade three-phase main control system of multi-station meter testing equipment (MTE) is presented. This design is based on the S3C2440 core board as the control core, and the software is based on Windows CE(WINCE) embedded operating system. The device is displayed and controlled by 7-inch Touch Screen. The main control system communicate with the error instrument and PC through Controller Area Network (CAN) bus, and the largest number of error instruments can connect to CAN bus is 100. The main control system communicates through RS232 bus with three-phase signal source and standard electric energy meter, through RS485 bus with programmable power amplifier. In this device, calibration of energy meters can not only through the PC software, but also use the main control system. Compared to the traditional design of the electric energy meter calibration device, the design’s the man-machine interface is more optimized, the number of electric energy meters can be test in the same time is more, faster communication, stronger anti-interference ability, and calibration is more efficiency.
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Mohamad, Khearia, Abduladhem Ali et R. Nagrajan. « Fuzzy-Neural Control of Hot-Rolling Mill ». Iraqi Journal for Electrical and Electronic Engineering 6, no 2 (1 décembre 2010) : 150–57. http://dx.doi.org/10.37917/ijeee.6.2.11.
Texte intégralRésumé :
This paper deals with the application of Fuzzy-Neural Networks (FNNs) in multi-machine system control applied on hot steel rolling. The electrical drives that used in rolling system are a set of three-phase induction motors (IM) controlled by indirect field-oriented control (IFO). The fundamental goal of this type of control is to eliminate the coupling influence though the coordinate transformation in order to make the AC motor behaves like a separately excited DC motor. Then use Fuzzy-Neural Network in control the IM speed and the rolling plant. In this work MATLAB/SIMULINK models are proposed and implemented for the entire structures. Simulation results are presented to verify the effectiveness of the proposed control schemes. It is found that the proposed system is robust in that it eliminates the disturbances considerably.
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Rezal, M., et D. Ishak. « Performance evaluation of multi-phase permanent magnet synchronous motor based on different winding configurations and magnetization patterns ». International Journal of Power Electronics and Drive Systems (IJPEDS) 10, no 3 (1 septembre 2019) : 1197. http://dx.doi.org/10.11591/ijpeds.v10.i3.pp1197-1206.
Texte intégralRésumé :
Permanent magnet synchronous motor (PMSM) is the most reliable and efficient machine that widely used in robotics and automation, industrial applications, electric vehicles, home appliances, aircraft and aerospace technology due to its high efficiency, good dynamic performance and high torque density. In this paper, the influence of various types of winding configuration and different magnetization patterns in the performance of a five-phase PMSM is investigated. Three types of magnetization patterns such as radial magnetization (RM), parallel magnetization (PaM), and multi-segmented Halbach magnetization (SH) are applied to the five-phase 10-slot/4-pole PMSM during open-circuit and on-load conditions. A 2D finite element method (FEM) is intensively used in this investigation to model and predict the electromagnetic characteristics and performance of the PMSM. The detailed results from the finite-element analysis (FEA) on the cogging torque, induced back-emf, airgap flux density and electromagnetic torque are analysed. The induced back-emf of the machine is computed further into its harmonic distortions. Additionally, the skewing method for minimization of cogging torque of PMSM is proposed. From the results, it is observed that the five-phase, 10-slot/4-pole PMSM with double layer distributed winding and parallel magnetization gives the best machine performance.
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Adda, Benkhalfallah, Hartani Kada, Norediene Aouadj et Karim Abdelkader Belhia. « New independent control of a Bi Machine system powered by a multi-leg inverter applied to four in-wheel motor drive electric vehicle ». International Journal of Power Electronics and Drive Systems (IJPEDS) 14, no 1 (1 mars 2023) : 614. http://dx.doi.org/10.11591/ijpeds.v14.i1.pp614-621.
Texte intégralRésumé :
The four-wheel-drive electric vehicle's traction chain is powered by four permanent-magnet synchronous motors (PMSM), powered by a single three-phase, five-leg inverter (FLI). In order to achieve the behavior of a mechanical differential and to require the parallel wheel-motors to turn at identical or different speeds, using this structure, an independent control is applied on each driving wheel. For this particular structure, there is a shared inverter leg between the two phases of two machines. The other two phases of each machine are attached to their own two inverter legs. This work's main focus is the suggestion of a new DTC technique for the control of an electric vehicle (EV) with two set bi-PMSM motor-wheels fed in parallel by a single three-phase five-leg inverter. The simulation results show that this new control technique can ensure excellent dynamics of the electric vehicle driving system.
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Ribeiro, Matheus, Stéfano Stefenon, José de Lima, Ademir Nied, Viviana Mariani et Leandro Coelho. « Electricity Price Forecasting Based on Self-Adaptive Decomposition and Heterogeneous Ensemble Learning ». Energies 13, no 19 (5 octobre 2020) : 5190. http://dx.doi.org/10.3390/en13195190.
Texte intégralRésumé :
Electricity price forecasting plays a vital role in the financial markets. This paper proposes a self-adaptive, decomposed, heterogeneous, and ensemble learning model for short-term electricity price forecasting one, two, and three-months-ahead in the Brazilian market. Exogenous variables, such as supply, lagged prices and demand are considered as inputs signals of the forecasting model. Firstly, the coyote optimization algorithm is adopted to tune the hyperparameters of complementary ensemble empirical mode decomposition in the pre-processing phase. Next, three machine learning models, including extreme learning machine, gradient boosting machine, and support vector regression models, as well as Gaussian process, are designed with the intent of handling the components obtained through the signal decomposition approach with focus on time series forecasting. The individual forecasting models are directly integrated in order to obtain the final forecasting prices one to three-months-ahead. In this case, a grid of forecasting models is obtained. The best forecasting model is the one that has better generalization out-of-sample. The empirical results show the efficiency of the proposed model. Additionally, it can achieve forecasting errors lower than 4.2% in terms of symmetric mean absolute percentage error. The ranking of importance of the variables, from the smallest to the largest is, lagged prices, demand, and supply. This paper provided useful insights for multi-step-ahead forecasting in the electrical market, once the proposed model can enhance forecasting accuracy and stability.
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Chen, Long, Xiaodong Sun, Haobin Jiang et Xing Xu. « A High-Performance Control Method of ConstantV/f-Controlled Induction Motor Drives for Electric Vehicles ». Mathematical Problems in Engineering 2014 (2014) : 1–10. http://dx.doi.org/10.1155/2014/386174.
Texte intégralRésumé :
A three-phase induction motor used as a propulsion system for the electric vehicle (EV) is a nonlinear, multi-input multi-output, and strong coupling system. For such a complicated model system with unmeasured and unavoidable disturbances, as well as parameter variations, the conventional vector control method cannot meet the demands of high-performance control. Therefore, a novel control strategy named least squares support vector machines (LSSVM) inverse control is presented in the paper. Invertibility of the induction motor in the constantV/fcontrol mode is proved to confirm its feasibility. The LSSVM inverse is composed of an LSSVM approximating the nonlinear mapping of the induction motor and two integrators. The inverse model of the constantV/f-controlled induction motor drive is obtained by using LSSVM, and then the optimal parameters of LSSVM are determined automatically by applying a modified particle swarm optimization (MPSO). Cascading the LSSVM inverse with the induction motor drive system, the pseudolinear system can be obtained. Thus, it is easy to design the closed-loop linear regulator. The simulation results verify the effectiveness of the proposed method.
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Lin, Ying-Dar, et Chun-Ying Huang. « Three-Phase Detection and Classification for Android Malware Based on Common Behaviors ». Journal of Communications Software and Systems 12, no 3 (21 septembre 2016) : 157. http://dx.doi.org/10.24138/jcomss.v12i3.80.
Texte intégralRésumé :
Android is one of the most popular operating systems used in mobile devices. Its popularity also renders it a common target for attackers. We propose an efficient and accurate three-phase behavior-based approach for detecting and classifying malicious Android applications. In the proposedapproach, the first two phases detect a malicious application and the final phase classifies the detected malware. The first phase quickly filters out benign applications based on requested permissions and the remaining samples are passed to the slower second phase, which detects malicious applications based on system call sequences. The final phase classifies malware into known or unknown types based on behavioral or permission similarities. Our contributions are three-fold: First, we propose a self-contained approach for Android malware identification and classification. Second, we show that permission requests from an Application are beneficial to benign application filtering. Third, we show that system call sequences generated from an application running inside a virtual machine can be used for malware detection. The experiment results indicate that the multi-phase approach is more accurate than the single-phase approach. The proposed approach registered true positive and false positive rates of 97% and 3%, respectively. In addition, more than 98% of the samples were correctly classified into known or unknown types of malware based on permission similarities.We believe that our findings shed some lights on future development of malware detection and classification.
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Zhang, Juchen, Shasha Song, Junsheng Zhang, Weijie Chang, Haidong Yang, Huohong Tang et Shunhua Chen. « Multi-Physics Coupling Modeling and Experimental Investigation of Vibration-Assisted Blisk Channel ECM ». Micromachines 13, no 1 (29 décembre 2021) : 50. http://dx.doi.org/10.3390/mi13010050.
Texte intégralRésumé :
Due to its advantages of good surface quality and not being affected by material hardness, electrochemical machining (ECM) is suitable for the machining of blisk, which is known for its hard-to-machine materials and complex shapes. However, because of the unstable processing and low machining quality, conventional linear feeding blisk ECM has difficulty in obtaining a complex structure. To settle this problem, the vibration-assisted ECM method is introduced to machine blisk channels in this paper. To analyze the influence of vibration on the process of ECM, a two-phase flow field model is established based on the RANS k-ε turbulence model, which is suitable for narrow flow field and high flow velocity. The model is coupled with the electric field, the flow field, and the temperature field to form a multi-physics field coupling model. In addition, dynamic simulation is carried out on account of the multi-physics field coupling model and comparative experiments are conducted using the self-developed ECM machine tool. While a shortcut appeared in the contrast experiment, machining with vibration-assisted channel ECM achieved fine machining stability and surface quality. The workpiece obtained by vibration-assisted channel ECM has three narrow and straight channels, with a width of less than 3 mm, an aspect ratio of more than 8, and an average surface roughness Ra in the hub of 0.327 μm. Compared with experimental data, the maximum relative errors of simulation are only 1.05% in channel width and 8.11% in machining current, which indicates that the multi-physics field coupling model is close to machining reality.
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Mahesh R N, Uma, et Anith Nelleri. « Multi-Class Classification and Multi-Output Regression of Three-Dimensional Objects Using Artificial Intelligence Applied to Digital Holographic Information ». Sensors 23, no 3 (17 janvier 2023) : 1095. http://dx.doi.org/10.3390/s23031095.
Texte intégralRésumé :
Digital holographically sensed 3D data processing, which is useful for AI-based vision, is demonstrated. Three prominent methods of learning from datasets such as sensed holograms, computationally retrieved intensity and phase from holograms forming concatenated intensity–phase (whole information) images, and phase-only images (depth information) were utilized for the proposed multi-class classification and multi-output regression tasks of the chosen 3D objects in supervised learning. Each dataset comprised 2268 images obtained from the chosen eighteen 3D objects. The efficacy of our approaches was validated on experimentally generated digital holographic data then further quantified and compared using specific evaluation matrices. The machine learning classifiers had better AUC values for different classes on the holograms and whole information datasets compared to the CNN, whereas the CNN had a better performance on the phase-only image dataset compared to these classifiers. The MLP regressor was found to have a stable prediction in the test and validation sets with a fixed EV regression score of 0.00 compared to the CNN, the other regressors for holograms, and the phase-only image datasets, whereas the RF regressor showed a better performance in the validation set for the whole information dataset with a fixed EV regression score of 0.01 compared to the CNN and other regressors.
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Owusu-Ansah, Ebenezer, et Colin Dalton. « Fabrication of a 3D Multi-Depth Reservoir Micromodel in Borosilicate Glass Using Femtosecond Laser Material Processing ». Micromachines 11, no 12 (6 décembre 2020) : 1082. http://dx.doi.org/10.3390/mi11121082.
Texte intégralRésumé :
Micromodels are ideal candidates for microfluidic transport investigations, and they have been used for many applications, including oil recovery and carbon dioxide storage. Conventional fabrication methods (e.g., photolithography and chemical etching) are beset with many issues, such as multiple wet processing steps and isotropic etching profiles, making them unsuitable to fabricate complex, multi-depth features. Here, we report a simpler approach, femtosecond laser material processing (FLMP), to fabricate a 3D reservoir micromodel featuring 4 different depths—35, 70, 140, and 280 µm, over a large surface area (20 mm × 15 mm) in a borosilicate glass substrate. The dependence of etch depth on major processing parameters of FLMP, i.e., average laser fluence (LFav), and computer numerically controlled (CNC) processing speed (PSCNC), was studied. A linear etch depth dependence on LFav was determined while a three-phase exponential decay dependence was obtained for PSCNC. The accuracy of the method was investigated by using the etch depth dependence on PSCNC relation as a model to predict input parameters required to machine the micromodel. This study shows the capability and robustness of FLMP to machine 3D multi-depth features that will be essential for the development, control, and fabrication of complex microfluidic geometries.
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Liang, Jinping, et Ke Zhang. « A New Hybrid Fault Diagnosis Method for Wind Energy Converters ». Electronics 12, no 5 (6 mars 2023) : 1263. http://dx.doi.org/10.3390/electronics12051263.
Texte intégralRésumé :
Fault diagnostic techniques can reduce the requirements for the experience of maintenance crews, accelerate maintenance speed, reduce maintenance cost, and increase electric energy production profitability. In this paper, a new hybrid fault diagnosis method based on multivariate empirical mode decomposition (MEMD), fuzzy entropy (FE), and an artificial fish swarm algorithm (AFSA)-support vector machine (SVM) is proposed to identify the faults of a wind energy converter. Firstly, the measured three-phase output voltage signals are processed by MEMD to obtain three sets of intrinsic mode functions (IMFs). The multi-scale analysis tool MEMD is used to extract the common modes matching the timescale. It studies the multi-scale relationship between three-phase voltages, realizes their synchronous analysis, and ensures that the number and frequency of the modes match and align. Then, FE is calculated to describe the IMFs’ complexity, and the IMFs-FE information is taken as fault feature to increase the robustness to working conditions and noise. Finally, the AFSA algorithm is used to optimize SVM parameters, solving the difficulty in selecting the penalty factor and radial basis function kernel. The effectiveness of the proposed method is verified in a simulated wind energy system, and the results show that the diagnostic accuracy for 22 fault modes is 98.7% under different wind speeds, and the average accuracy of 30 running can be maintained above 84% for different noise levels. The maximum, minimum, average, and standard deviation are provided to prove the robust and stable performance. Compared with the other methods, the proposed hybrid method shows excellent performance in terms of high accuracy, strong robustness, and computational efficiency.
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Echeikh, H., R. Trabelsi, Atif Iqbal, M. F. Mimouni et R. Alammari. « Online Adaptation of Rotor Resistance based on Sliding Mode Observer with Backstepping Control of A Five-Phase Induction Motor Drives ». International Journal of Power Electronics and Drive Systems (IJPEDS) 7, no 3 (1 septembre 2016) : 648. http://dx.doi.org/10.11591/ijpeds.v7.i3.pp648-655.
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Multiphase electric drives have been developed due to numerous advantages offered by those machines when it compared with the conventional three-phase machines. Multiphase motor drives are considered for applications, where the reduction of power per phase for both motor and inverter and high reliability are required. High performance control techniques are developed for multi-phase drives. The performances of the high performance controller and flux observers may be degraded during the operation. Since the parameters of Induction Motor (IM) varies continuously due to temperature variation and heating. Thus it is significantly important that the value of rotor resistance is continuously observed online and adapted by the control algorithm in order to avoid detuning effects. The efficiency and performance of an induction motor drive system can be improved by online observation of the critical parameters, such as the rotor resistance and stator resistance. Among the parameters of IM, rotor resistance is a decisive one for flux estimation, and also the stator resistance becomes critical in the low-speed operation condition. This paper presents a new online estimation method for the rotor resistance of the IM for sliding mode observer. This method generally based on theories of variable structure and is useful in order to adjust online unknown parameters (load torque and rotor resistance). The presented non-linear compensator afford a voltage inputs on the articulation of stator current and rotor speed measurements, and engender an estimates for the unknown parameters simultaneously, the non-measurable state variables (rotor flux and derivatives of the stator current and voltage) that converge to the corresponding true values. Under the persistent excitation condition, the proposed method estimates the actual value of rotor resistance, which guarantees the exact estimation of the rotor flux. Non-linear Backstepping control and adaptive sliding mode observer of a five-phase induction motor drive is presented. The accuracy and validity of the method is verified by MATLAB simulation model.
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Omar, Riyadh G. « Coast function parameters optimization for DC battery source inverter feeding three-phase inductive load ». International Journal of Power Electronics and Drive Systems (IJPEDS) 11, no 4 (1 décembre 2020) : 1799. http://dx.doi.org/10.11591/ijpeds.v11.i4.pp1799-1804.
Texte intégralRésumé :
The commonly reported measures of the predictive accuracy are evaluated in this paper. Absolute, squared, percentage, and integral errors methods are implemented, to reduce the objective function, which employed in model predictive control. These methods are usually investigated for dc source inverter, which controlled by finite set model predictive current control system, with three phase induction motor load. In this paper, the evaluation includes different aspects, accuracy, complexity, system harmonics content, and execution time. A vital criterion in this process is the performance of the inverter, and the matching between the reference and the measured machine currents. The evaluation shows that for one term objective function, absolute and square errors give similar results with less execution time for the absolute error, but if multi terms objective function the square error is better.
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Korkosz, Mariusz, Piotr Bogusz et Jan Prokop. « The Fault-Tolerant Quad-Channel Brushless Direct Current Motor ». Energies 12, no 19 (25 septembre 2019) : 3667. http://dx.doi.org/10.3390/en12193667.
Texte intégralRésumé :
In this study, a permanent magnet brushless direct current machine with multi-phase windings is proposed for critical drive systems. We have named the solution, which has four-stator winding, a quad-channel permanent magnet brushless direct current (QCBLDC) motor. The stator windings are supplied by four independent power converters under quad-channel operation (QCO) mode. After a fault in either one, two, or three channels, further operation of the machine can be continued in triple-channel operation (TCO) mode, dual-channel operation (DCO) mode, or single-channel operation (SCO) mode. In this paper, a novel mathematical model is proposed for a QCBLDC machine. This model takes into account the nonlinearity of a magnetic circuit and all of the couplings between the phases within a given channel, as well as between channels. Based on numerical calculations, the static electromagnetic moment and the coupled fluxes were determined for the individual windings of the variants and work modes being analyzed. A normal work condition can be achieved in the QCO or DCO modes. For the DCO mode, an acceptable case uses a balanced magnetic pull (A and C channels supplied). The DCO A and B type work mode is comparable to the DCO A and C mode with regard to its efficiency in processing electrical energy. The vibroacoustic parameters of this mode, however, are much worse. In fault states, TCO, DCO, and SCO work modes are possible. As the number of active channels decreases, the efficiency of energy processing also decreases. In a critical situation, the motor works in overload mode (SCO mode). Laboratory tests conducted for one of the variants demonstrated that the TCO work mode is characterized by worse vibroacoustic parameters than the DCO A and C mode.
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Zadehbagheri, Mahmoud, Tole Sutikno, Mohammad Javad Kiani et Meysam Yousefi. « Designing a power system stabilizer using a hybrid algorithm by genetics and bacteria for the multi-machine power system ». Bulletin of Electrical Engineering and Informatics 12, no 3 (1 juin 2023) : 1318–31. http://dx.doi.org/10.11591/eei.v12i3.4704.
Texte intégralRésumé :
This research creates an optimal power grid stabilizer for four machines. Rotor speed signals are system inputs in the proposed design. To improve response, this system uses a conventional power system stabilizer (CPSS) and an optimal CPSS (OCPSS). The genetic optimization hybrid has a new structure, and the network generators use the bacterial foraging algorithm (BFA) with stabilizer system. The system set is tested by MATLAB software under various conditions to evaluate the designs. The experiment starts with a three-phase fault in line 3 that is fixed by breaking the line after 0.2 seconds. The simulation results show that after a short circuit in line 3, the proposed OCPSS design reaches damping after about a cycle of oscillation in 4 seconds. However, the conventional CPSS design achieves damping after four oscillations in six seconds. Simulations show that the proposed method is better than genetic algorithm (GA) and BFA. Power system oscillations are dampened faster and with lower amplitude when power system stabilizer (PSS) coordinate with the proposed optimization method. It also improves power system dynamics. We demonstrate with the proposed OCPSS stabilizer that advanced optimization systems can maximize system control capacity by utilizing conventional CPSS system advantages.
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Aldrees, Ali, Mohsin Ali Khan, Muhammad Atiq Ur Rehman Tariq, Abdeliazim Mustafa Mohamed, Ane Wai Man Ng et Abubakr Taha Bakheit Taha. « Multi-Expression Programming (MEP) : Water Quality Assessment Using Water Quality Indices ». Water 14, no 6 (17 mars 2022) : 947. http://dx.doi.org/10.3390/w14060947.
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Water contamination is indeed a worldwide problem that threatens public health, environmental protection, and agricultural productivity. The distinctive attributes of machine learning (ML)-based modelling can provide in-depth understanding into increasing water quality challenges. This study presents the development of a multi-expression programming (MEP) based predictive model for water quality parameters, i.e., electrical conductivity (EC) and total dissolved solids (TDS) in the upper Indus River at two different outlet locations using 360 readings collected on a monthly basis. The optimized MEP models were assessed using different statistical measurements i.e., coefficient-of-determination (R2), root-mean-square error (RMSE), mean-absolute error (MAE), root-mean-square-logarithmic error (RMSLE) and mean-absolute-percent error (MAPE). The results show that the R2 in the testing phase (subjected to unseen data) for EC-MEP and TDS-MEP models is above 0.90, i.e., 0.9674 and 0.9725, respectively, reflecting the higher accuracy and generalized performance. Also, the error measures are quite lower. In accordance with MAPE statistics, both the MEP models shows an “excellent” performance in all three stages. In comparison with traditional non-linear regression models (NLRMs), the developed machine learning models have good generalization capabilities. The sensitivity analysis of the developed MEP models with regard to the significance of each input on the forecasted water quality parameters suggests that Cl and HCO3 have substantial impacts on the predictions of MEP models (EC and TDS), with a sensitiveness index above 0.90, although the influence of the Na is the less prominent. The results of this research suggest that the development of intelligence models for EC and TDS are cost effective and viable for the evaluation and monitoring of the quality of river water.
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Agarala, Ajaysekhar, Sunil S. Bhat, Arghya Mitra, Daria Zychma et Pawel Sowa. « Transient Stability Analysis of a Multi-Machine Power System Integrated with Renewables ». Energies 15, no 13 (1 juillet 2022) : 4824. http://dx.doi.org/10.3390/en15134824.
Texte intégralRésumé :
The impact on the stability of power systems is rising as the penetration level of renewable energy with sporadic natures rises rapidly on the grid. However, the impact of different types of renewable energy sources (wind, solar) and their combination on system stability varies even with the same penetration level. This paper concentrates mainly on the stability analysis of multi-machine systems connected to various types of renewable energy sources. The study presents a simple and novel control technique named automatic reactive power support (ARS) for both single and combinations of renewable sources by injecting the available reactive power into the system during fault through converters to enhance system stability. The permanent magnet synchronous generator (PMSG) and doubly fed induction generator (DFIG) are both considered as wind generators in this paper for comparison. In addition, transient stability enhancement is carried out by improving critical clearing time of a three-phase fault in the power system. With the creation of a 3-phase fault at various buses, stability analysis is carried out on the 9-bus WSCC test bus system and also on the 68-bus IEEE test system. Comparative analysis of six test case conditions is provided and the considered cases are without renewable source, with DFIG as a wind generator, PMSG as a wind generator, solar PV farm, wind farm with DFIG and solar PV in combination and the combination of wind farm with PMSG and solar PV. Moreover, the improvement in critical clearing time of the system is compared using conventional and proposed controls with all the aforementioned renewable sources. Comparative results show that the proposed control technique improves system stability and also that the combination of renewable energy sources ought to enhance the critical clearing time of system.
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Prinzi, Francesco, Carmelo Militello, Vincenzo Conti et Salvatore Vitabile. « Impact of Wavelet Kernels on Predictive Capability of Radiomic Features : A Case Study on COVID-19 Chest X-ray Images ». Journal of Imaging 9, no 2 (30 janvier 2023) : 32. http://dx.doi.org/10.3390/jimaging9020032.
Texte intégralRésumé :
Radiomic analysis allows for the detection of imaging biomarkers supporting decision-making processes in clinical environments, from diagnosis to prognosis. Frequently, the original set of radiomic features is augmented by considering high-level features, such as wavelet transforms. However, several wavelets families (so called kernels) are able to generate different multi-resolution representations of the original image, and which of them produces more salient images is not yet clear. In this study, an in-depth analysis is performed by comparing different wavelet kernels and by evaluating their impact on predictive capabilities of radiomic models. A dataset composed of 1589 chest X-ray images was used for COVID-19 prognosis prediction as a case study. Random forest, support vector machine, and XGBoost were trained (on a subset of 1103 images) after a rigorous feature selection strategy to build-up the predictive models. Next, to evaluate the models generalization capability on unseen data, a test phase was performed (on a subset of 486 images). The experimental findings showed that Bior1.5, Coif1, Haar, and Sym2 kernels guarantee better and similar performance for all three machine learning models considered. Support vector machine and random forest showed comparable performance, and they were better than XGBoost. Additionally, random forest proved to be the most stable model, ensuring an appropriate balance between sensitivity and specificity.
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Parathai, Phetcharat, Naruephorn Tengtrairat, Wai Lok Woo, Mohammed A. M. Abdullah, Gholamreza Rafiee et Ossama Alshabrawy. « Efficient Noisy Sound-Event Mixture Classification Using Adaptive-Sparse Complex-Valued Matrix Factorization and OvsO SVM ». Sensors 20, no 16 (5 août 2020) : 4368. http://dx.doi.org/10.3390/s20164368.
Texte intégralRésumé :
This paper proposes a solution for events classification from a sole noisy mixture that consist of two major steps: a sound-event separation and a sound-event classification. The traditional complex nonnegative matrix factorization (CMF) is extended by cooperation with the optimal adaptive L1 sparsity to decompose a noisy single-channel mixture. The proposed adaptive L1 sparsity CMF algorithm encodes the spectra pattern and estimates the phase of the original signals in time-frequency representation. Their features enhance the temporal decomposition process efficiently. The support vector machine (SVM) based one versus one (OvsO) strategy was applied with a mean supervector to categorize the demixed sound into the matching sound-event class. The first step of the multi-class MSVM method is to segment the separated signal into blocks by sliding demixed signals, then encoding the three features of each block. Mel frequency cepstral coefficients, short-time energy, and short-time zero-crossing rate are learned with multi sound-event classes by the SVM based OvsO method. The mean supervector is encoded from the obtained features. The proposed method has been evaluated with both separation and classification scenarios using real-world single recorded signals and compared with the state-of-the-art separation method. Experimental results confirmed that the proposed method outperformed the state-of-the-art methods.
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Modran, Horia Alexandru, Tinashe Chamunorwa, Doru Ursuțiu, Cornel Samoilă et Horia Hedeșiu. « Using Deep Learning to Recognize Therapeutic Effects of Music Based on Emotions ». Sensors 23, no 2 (14 janvier 2023) : 986. http://dx.doi.org/10.3390/s23020986.
Texte intégralRésumé :
Music is important in everyday life, and music therapy can help treat a variety of health issues. Music listening is a technique used by music therapists in various clinical treatments. As a result, music therapists must have an intelligent system at their disposal to assist and support them in selecting the most appropriate music for each patient. Previous research has not thoroughly addressed the relationship between music features and their effects on patients. The current paper focuses on identifying and predicting whether music has therapeutic benefits. A machine learning model is developed, using a multi-class neural network to classify emotions into four categories and then predict the output. The neural network developed has three layers: (i) an input layer with multiple features; (ii) a deep connected hidden layer; (iii) an output layer. K-Fold Cross Validation was used to assess the estimator. The experiment aims to create a machine-learning model that can predict whether a specific song has therapeutic effects on a specific person. The model considers a person’s musical and emotional characteristics but is also trained to consider solfeggio frequencies. During the training phase, a subset of the Million Dataset is used. The user selects their favorite type of music and their current mood to allow the model to make a prediction. If the selected song is inappropriate, the application, using Machine Learning, recommends another type of music that may be useful for that specific user. An ongoing study is underway to validate the Machine Learning model. The developed system has been tested on many individuals. Because it achieved very good performance indicators, the proposed solution can be used by music therapists or even patients to select the appropriate song for their treatment.
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Hagengruber, Annette, Ulrike Leipscher, Bjoern M. Eskofier et Jörn Vogel. « A New Labeling Approach for Proportional Electromyographic Control ». Sensors 22, no 4 (10 février 2022) : 1368. http://dx.doi.org/10.3390/s22041368.
Texte intégralRésumé :
Different control strategies are available for human machine interfaces based on electromyography (EMG) to map voluntary muscle signals to control signals of a remote controlled device. Complex systems such as robots or multi-fingered hands require a natural commanding, which can be realized with proportional and simultaneous control schemes. Machine learning approaches and methods based on regression are often used to realize the desired functionality. Training procedures often include the tracking of visual stimuli on a screen or additional sensors, such as cameras or force sensors, to create labels for decoder calibration. In certain scenarios, where ground truth, such as additional sensor data, can not be measured, e.g., with people suffering from physical disabilities, these methods come with the challenge of generating appropriate labels. We introduce a new approach that uses the EMG-feature stream recorded during a simple training procedure to generate continuous labels. The method avoids synchronization mismatches in the labels and has no need for additional sensor data. Furthermore, we investigated the influence of the transient phase of the muscle contraction when using the new labeling approach. For this purpose, we performed a user study involving 10 subjects performing online 2D goal-reaching and tracking tasks on a screen. In total, five different labeling methods were tested, including three variations of the new approach as well as methods based on binary labels, which served as a baseline. Results of the evaluation showed that the introduced labeling approach in combination with the transient phase leads to a proportional command that is more accurate than using only binary labels. In summary, this work presents a new labeling approach for proportional EMG control without the need of a complex training procedure or additional sensors.
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Varone, Giuseppe, Wadii Boulila, Michele Lo Giudice, Bilel Benjdira, Nadia Mammone, Cosimo Ieracitano, Kia Dashtipour et al. « A Machine Learning Approach Involving Functional Connectivity Features to Classify Rest-EEG Psychogenic Non-Epileptic Seizures from Healthy Controls ». Sensors 22, no 1 (25 décembre 2021) : 129. http://dx.doi.org/10.3390/s22010129.
Texte intégralRésumé :
Until now, clinicians are not able to evaluate the Psychogenic Non-Epileptic Seizures (PNES) from the rest-electroencephalography (EEG) readout. No EEG marker can help differentiate PNES cases from healthy subjects. In this paper, we have investigated the power spectrum density (PSD), in resting-state EEGs, to evaluate the abnormalities in PNES affected brains. Additionally, we have used functional connectivity tools, such as phase lag index (PLI), and graph-derived metrics to better observe the integration of distributed information of regular and synchronized multi-scale communication within and across inter-regional brain areas. We proved the utility of our method after enrolling a cohort study of 20 age- and gender-matched PNES and 19 healthy control (HC) subjects. In this work, three classification models, namely support vector machine (SVM), linear discriminant analysis (LDA), and Multilayer perceptron (MLP), have been employed to model the relationship between the functional connectivity features (rest-HC versus rest-PNES). The best performance for the discrimination of participants was obtained using the MLP classifier, reporting a precision of 85.73%, a recall of 86.57%, an F1-score of 78.98%, and, finally, an accuracy of 91.02%. In conclusion, our results hypothesized two main aspects. The first is an intrinsic organization of functional brain networks that reflects a dysfunctional level of integration across brain regions, which can provide new insights into the pathophysiological mechanisms of PNES. The second is that functional connectivity features and MLP could be a promising method to classify rest-EEG data of PNES form healthy controls subjects.