Search

International audience; This paper presents the modeling and control of an Electric Vehicle (EV) driven by a 5-phase Permanent Magnet Synchronous Machine (PMSM). The machine in an open-end winding configuration is supplied by two isolated 5-leg Voltage Source Inverters (VSIs). The inverters are fed by a Hybrid Energy Storage System (HESS) consisting of a battery and Supercapacitors (SCs).To increase the battery lifetime, the SCs are used to provide the high-power requirement during driving operations. In addition, an Energy Management Strategy (EMS) is proposed to take advantages of this EV architecture. Numerical results are derived to verify workability of the EV system.

For an open-end windings integrated Permanent Magnet Synchronous Machine, the demagnetization of the permanent magnets is analyzed when a transistor is short-circuited and no specific control strategy is adopted. Depending on the temperature, the high currents due to the inverter fault may locally demagnetized the permanent magnets leading to an accelerated aging of the machine and torque loss. A co-simulation, using a Finite Element software for the machine coupled with an average modeling of the transistor, gives interesting local prediction of the machine behavior in healthy and degraded mode. This work has been achieved within the framework of CE2I project. CE2I is co-financed by European Union with the financial support of European Regional Development Fund (ERDF), French State and the French Region of Hauts-de-France.

This paper proposes a novel variable speed control strategy of a particular 5-phase Permanent Magnet Synchronous Generator (PMSG) in healthy and faulty modes by taking into account the constraints on voltages and currents. These constraints are related to the converter and machine design. The considered faults are open-circuited phases (one phase, two adjacent phases and two non-adjacent phases). A variable speed control strategy is presented, including flux weakening operations. Based on analytical formulations, a numerical computation is proposed to bring out the torque−speed characteristics. This method allows the determination of the current references which ensure the functioning of a 5-phase PMSG at variable speed while keeping phase voltages and currents below their limits. Theoretical, numerical and experimental results are presented. These results are compared in order to validate the proposed approach.

This work is a part of a project CE2I supported by FEDER and by the regional council of Hauts de France Region. It has been also supported by Thales Alenia Space. This paper deals with the fault effects analysis and diagnosis in 6-Φ PMSM designed for aerospace applications. The addressed work aims to analyze the features offered by the space vector theory applied to these systems for fault detection and identification purposes. The paper starts with a presentation of the overall electric drive system structure and its control. Then, fault effects analysis under faulty operation mode of the 6-leg voltage source inverter is presented considering the space vector theory. Based on such analysis, an accurate FDI process is designed for these applications. All results are verified analytically and through simulation software using Matlab/Simulink. Thales Group and Regional Council of Hauts de France

This paper analyzes two fault-tolerant dualmultiphase motor drives, a series connected topology and a standard H-bridge topology. Previous studies have shown that the series connected topology is appropriate to an aerospace application and has lower peak current in degraded mode in comparison with the H-bridge topology, which may consequently diminish the system’s weight and cost. This paper extends the study to compare different control strategies of these structures under two fault conditions: short-circuit of an inverter’s switch and an open-phase of the machine. The control strategies analyzed in this paper do not impact the fundamental current or the torque generation, but the amplitudes of some harmonics in degraded mode are expected to be narrowed down in order to reduce the inverter’s size. Some analyses of maximum voltage and peak current in degraded mode have been used for inverter dimensioning. Experimental results are shown and compared to the simulated ones to confirm the validity of this study. Thales Avionics Electrical Systems

International audience; This paper proposes a procedure that is suitable for experimental investigation of real-time open-switch and open-phase faults diagnosis of a five-leg Voltage Source Inverter (VSI) feeding a five-phase bi-harmonic Permanent Magnet Synchronous Machine (5-Φ B-PMSM). The algorithm is based on the specific characteristics of multiphase machines, which allows inverter fault detection with sufficient robustness of the algorithm in the presence of fundamental and third harmonic components. Firstly, the inverter fault effects analysis is achieved in the characteristic subspaces of the five-phase PMSM. Specificities that are interesting for the elaboration of a real-time Fault Detection and Identification (FDI) process are highlighted. Original and particular algorithms are used for an accurate two-dimensional normalized fault vector extraction in a defined fault reference frame. This frame is dedicated only for fault detection and identification. To ensure the high immunity of the FDI process against transient states, a particular normalization procedure is applied. The normalized diagnostic signals are formulated from the defined frame and others variables derived from the reference and measured currents. Simulation and experimental results of open-switch and open-phase faults are provided to validate the proposed algorithm.

This paper proposes a new method based on Artificial Neural Networks for reducing the torque ripple in a non-sinusoidal Synchronous Reluctance Motor. The Lagrange optimization method is used to solve the problem of calculating optimal currents in the d-q frame. A neural control scheme is then proposed as an adaptive solution to derive the optimal stator currents giving a constant electromagnetic torque and minimizing the ohmic losses. Thanks to the online learning capacity of neural networks, the optimal currents can be obtained online in real time. With this neural control, each machine’s parameters estimation errors and current controller errors can be compensated. Simulation and experimental results are presented which confirm the validity of the proposed method. Bourse de l'Ambassade de France au Vietnam

This papers presents a sensorless control for five-phase PM synchronous machines. An adaptive method, based on a linear neural network called Adaline (Adaptive Linear Neural Networks), has been achieved to estimate the rotor position with a high precision even at low speed without high frequency signal injection. Non-sinusoidal three-phase PM machines require more complex algorithm for sensorless control because of harmonics in the back-EMF. This is not the case for multiphase PM machines thanks to the property of equivalent machines in the eigenspace. Some given simulation and experimental results in laboratory confirm the possibility of real-time implementation of Adaline networks and the good performance of sensorless control based on this.

This paper aims to evaluate some configurations of windings for low speed high power 5-phase permanent magnet synchronous generators (PMSG) associated to fixed pitch tidal turbines. Several Fractional-Slot Concentrated- Windings has been considered and compared for these specifications. The proposed structures are compared in terms of torque VS speed characteristics, including flux weakening operation, in healthy and faulty modes. In fault mode, the failure of one and two phases is considered. The results show that concentrated windings can be particularly useful for these particular specifications.

This paper deals with the real time Fault Detection and Identification (FDI) process of inverter Open Switch Fault (OSF) and Open Phase Fault (OPF) in five-phase PMSM designed for aerospace applications in which the electric drive system has particular operating characteristics either in healthy states or in the faulty ones. Two original contributions are considered in this paper. They consist in normalizing the input variables of the FDI process applied to multiphase system and compensating the noise (switching and sensors noises) and dccomponent resulting from the fault occurrence. The proposed strategy uses multiple normalized criterias derived from the measured phase currents and the references currents obtained from the outputs of the speed controllers. The FDI shows independence with respect to transient states and to switching and measurement noises. Moreover, it can be easily included in an existing software without any additional sensors. The validity of the proposed method is verified by Matlab/Simulink simulation tests. Projet "3MToletance" financé par la Région Hauts de France

A novel centroid-based diagnostic method of the power switches in five-leg Voltage Source Inverter (VSI) is proposed in this paper. Using a vectorial multi-machine description, a five-phase drive presenting an opened switch or an opened phase faults has typical operating characteristics in comparison to classical three-phase drives. Based on such characteristics, this work aims to provide a simple and robust diagnostic process for switches fault regardless of the shape of the back-EMFs (harmonic components) and the transient states due to the load variation. Original theoretical developments are presented. Experimental results are shown to validate the proposed strategy 3M-Tolerant Project of MEDEE funded par Region Nord-Pas-de Calais

The optimal control of electrical drives necessitates to take into account current and voltage limits that are imposed by the power electronics and the electrical machines. Let’s cite for example the flux-weakening operation of electrical drives for propulsion. If the control of classical three-phase drives Under voltage and current limits are known for a long time, the specific characteristics of multiphase drives open the way to researches on their control under such constraints. This paper aims to explain what are the main differences between three-phase and multiphase drives when they run under voltage and current constraints and try to show what are the scientific and technical problems to be solved. Some first results are given in order to show that Model Predictive Control (MPC) is expected to be a good candidate to answer the proposed challenge.; International audience; The optimal control of electrical drives necessitates to take into account current and voltage limits that are imposed by the power electronics and the electrical machines. Let’s cite for example the flux-weakening operation of electrical drives for propulsion. If the control of classical three-phase drives Under voltage and current limits are known for a long time, the specific characteristics of multiphase drives open the way to researches on their control under such constraints. This paper aims to explain what are the main differences between three-phase and multiphase drives when they run under voltage and current constraints and try to show what are the scientific and technical problems to be solved. Some first results are given in order to show that Model Predictive Control (MPC) is expected to be a good candidate to answer the proposed challenge.

International audience; Multi-phase machines are well-known for their fault tolerant capability. Star-connected multiphase machines have fault tolerance in open-circuit. For inverter switch short-circuitfault, it is possible to keep a smooth torque of Permanent Magnet Synchronous Machine (PMSM) if the currents of faulty phases are determined and their values are acceptable. This paper investigates fault-tolerant operations of an open-end fivephasedrive, i.e. a multi-phase machine fed with a dual-inverter supply. Inverter switch short-circuit fault is considered and handled with a simple solution. Original theoretical developments are presented. Simulation and experimental results validate theproposed strategy.

International audience; This paper analyzes two dual-motor fault-tolerant topologies for aerospace thruster application. The first structure supplies independently both machines while the second one connects them in series for reducing the number of transistors and offering a capability of energy management between the sources. Inverter short-circuit fault is considered. Based on the peak-currents obtained in simulation in degraded mode without reconfiguration and with two different reconfiguration strategies, the two proposed topologies can be compared in economic and technical aspects.

International audience; This paper analyzes two dual-motor fault-tolerant topologies. The first one supplies independently both machines while the second one connects them in series for reducing the number of transistors. For a given DC-link voltage, the converter component sizing is based on the peak current obtained in the normal and degraded modes.

International audience; An original analytical expression is presented in this paper to obtain optimal currents minimizing the copper losses of a multi-phase Permanent Magnet Synchronous Motor (PMSM) under fault conditions. Based on the existing solutions [i]opt1 (without zero sequence of current constraint) and [i]opt2 (with zero sequence constraint), this new expression of currents [i]opt3 is obtained by means of a geometrical representation and can be applied to open-circuit, defect of current regulation, current saturation and machine phase short-circuit fault. Simulation results are presented to validate the proposed approach.

All-electric vehicles should be made more reliable in such way one can continue driving even after the event of one or more faults in the electrical drive. For this, the use of multiphase (more than 3 phases) electrical machines is considered here due to its redundant feature. An additional technique to increase reliability is to replace sensors by observers. This paper studies the feasibility to control the torque in multi-phase permanentmagnet synchronous drives without a position sensor, with the lowest Joule losses, and this for a healthy as well as for a faulty operation with one or more phases open-circuited

International audience; This paper presents an original method, based on artificial neural networks, to reduce the torque ripple in a permanent-magnet nonsinusoidal synchronous motor. Solutions for calculating optimal currents are deduced from geometrical considerations and without a calculation step, which is generally based on the Lagrange optimization. These optimal currents are obtained from two hyperplanes. This paper takes into account the presence of harmonics in the back-EMF and the cogging torque. New control schemes are thus proposed to derive the optimal stator currents giving exactly the desired electromagnetic torque (or speed) and minimizing the ohmic losses. The torque and the speed control scheme both integrate two neural blocks, one dedicated for optimal-current calculation and the other to ensure the generation of these currents via a voltage source inverter. Simulation and experimental results from a laboratory prototype are shown to confirm the validity of the proposed neural approach.

International audience; This paper presents a new method based on Artificial Neural Networks to obtain the optimal currents, for reducing the torque ripple in a Non-sinusoidal Synchronous Reluctance Motor. Optimal current control has to develop a constant electromagnetic torque and minimize the ohmic losses. In d-q reference frame without homopolar current, the direct and quadrature optimal currents will be determined thanks to Lagrange optimization. A neural control scheme is then proposed as an adaptive solution to derive the optimal stator currents. Thanks to learning capacity of neural networks, the optimal currents will be obtained online. With this neural control, either machine's parameters estimation errors or current controller errors can be compensated. Simulation results using Matlab/Simulink are presented to confirm the validity of the proposed method.

International audience; This paper presents a new method for online symmetrical components and phase-angle extraction from high-voltage transmission-line faults. This method is based on the Adaline neural networks and the instantaneous power theory, also known as the method. A new current decomposition is proposed in order to derive the direct, inverse, and homopolar current components. The average and oscillating terms of powers in the frame are separated by using four Adaline neural networks. The Adalines use a cosine and sine as inputs in order to learn the linear combination of the powers. The resulting symmetrical components are used by three other Adalines for phase-angle estimation between direct and inverse current components. These phase angles permit classifying the fault types. The neural networks use an online learning process-based Widrow-Hoff algorithm and can adapt their weight parameters to the power-supply evolution. Simulation results show the performance and the robustness of this method and provide a perspective for protection relay improvement.

International audience; This study proposes several high precision selective harmonics compensation schemes for an active power filter. Harmonic currents are identified and on-line tracked by novel Adaline-based architectures which work in different reference-frames resulting from specific currents or powers decompositions. Adalines are linear and adaptive neural networks which present an appropriate structure to fit and learn a weighted sum of terms. Sinusoidal signals with a frequency multiple of the fundamental frequency are synthesized and used as inputs. Therefore, the amplitude of each harmonic term can be extracted separately from the Adaline weights adjusted with a recursive LMS (Least Mean Squares) algorithm. A first method is based on the modified instantaneous powers, a second method optimizes the active currents, and a third method relies on estimated fundamental currents synchronized with the direct voltage components. By tracking the fluctuating harmonic terms, the Adalines learning process allows the compensation schemes to be well suited for on-line adaptive compensation. Digital implementations of the identification schemes are performed and their effectiveness is verified by experiments.

This paper will consider the benefits of neural controllers over model-based current regulators to supervise the current generation of a shunt active power filter. The task consists in generating appropriate compensation currents with a system composed of a voltage source inverter and a low-pass filter. These currents cancel the harmonic terms introduced by nonlinear loads in a power distribution grid. The performances of conventional controllers such as a PI and resonant current regulators are confronted to neural controllers. If conventional regulators present some advantages in terms of engineering specifications, their tuning remains difficult and their design relies on a rough linearization of the system. Their performances are acceptable without perturbations. However, fast changes of nonlinear loads lead to different operating points of the system. Furthermore, the inverter's nonlinearities and low-pass filter parameters have to be considered for generating precise currents. Two neural approaches have therefore been proposed, one which estimates the input-output relationship of the system, and one which relies on a state-space representation of the system. These approaches combine learning capabilities with a priori knowledge of the system. The benefits of the neural approaches are discussed and illustrated by simulations and by experimental tests with real-time implementations on a digital signal processing board.

The application of neural networks to power systems has been extensively reported. Neural networks based protection techniques have been proposed by a number of authors. However, almost all the studies have so far employed the back-propagation neural network structure with supervised learning. This paper presents an on line method for fault identification in Electrical High Voltage (EHV) transmission line. This approach utilizes linear adaptive neuron, which is called Adaline. The Adaline neural network is generally used for prediction and identification problems and is rarely used for power system protection. Using current signals, the Adaline process has a strong tracking capability and is fast due to its simple construction, which makes it more suitable for the implementation. Our Adaline approach is compared with a multilayer perceptron in order to see the influence of the fault resistance over the fault time detection.

An novel multiplexing technique applied on a neural harmonics extraction method is presented in this paper. This structure can be used in nonlinear loads compensation with Active Power Filters. The approach is composed of a neural Phase Lock-Loop and a neural reference current generator based on an efficient formulation of the instantaneous reactive power theory. For the purpose of harmonics suppression and reactive power compensation, the whole architecture is composed of three Adaline Neural Networks whose structure leads to an important consumption of Field Programmable Gate Array resources during implementation. The presented technique uses only one Adaline and keeps the immunity of the proposed approach under non-sinusoidal and unbalanced conditions of voltage. Simulation results of the neural harmonics detection system connected to a reference current control shows balanced and sinusoidal source currents under various conditions. Results with experimental measurement made on an Active Power Filters test bench demonstrate its good performances on harmonics filtering. Moreover, the simple structure from the new approach called mp-q method shows a significant resource reduction.

In this paper, two Adaline-based schemes for identifying the harmonic currents generated by nonlinear loads in power distribution systems are presented. Each neural method is based on formalized expressions of the active and reactive instantaneous powers. In both methods, the expressions of the instantaneous powers are learned on-line by Adalines. The amplitude of each harmonic term is thus individually estimated with the Adalines weights. This allows to calculate the compensating currents of an active power filter in real-time. The efficiency of the active power filter using the two proposed neural schemes is demonstrated in terms of total harmonic distortion and power factor with experimental results under various operating conditions. The performance of the neural schemes are favorably compared to other methods.

An original three-phase neural approach for phase and symmetrical components estimation is proposed in this paper. This neural structure can be used for power quality control, in an active power filtering scheme for example. The approach is composed of a neural symmetrical voltage components extraction and a neural phase detection technique. These functional tasks are decomposed and approximated by the learning of Adaline neural networks. The whole neural architecture is implemented on a Digital Signal Processor (DSP) and is applied to a three-phase power system. The presented analysis substantiates the immunity of the proposed approach under distorted utility conditions. Experimental results, under various utility conditions, demonstrate its phase tracking and voltage components extraction abilities. Its performance and robustness are also compared to a conventional Phase-Lock Loop (PLL) on a three-phase power supply with varying parameters and distorted by harmonics and random noise.

This paper presents a new harmonic currents identification method called neural synchronous method and based on artificial neural networks. Its theoretical aspect relies on a new decomposition of the load current signals. Adaline neural networks are used in order to learn this decomposition on-line; the fundamental currents can therefore be estimated at each sampling time. The fundamental currents are then synchronized with the direct component of the voltage obtained by a PLL (phase locked loop). The harmonic currents are deduced and re-injected phase-opposite in the power distribution system through an active power filtering scheme. This harmonic currents identification method is compared to other similar methods by simulation results.