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This paper presents the results of a time-domain identification procedure to estimate the linear parameters of a salient-pole synchronous machine at standstill. The objective of this study is to use PWM input signal to identify the model structure and parameters of a salient-pole synchronous machine from standstill test data. The procedure consists to define, to conduct the standstill tests and also to identify the model structure. The signals used for identification are the excitation voltages at standstill and the flowing current in different windings. We estimate the parameters of operational impedances, or in other words the reactance and the time constants. The tests were carried out on synchronous machine of 1.5 kVA 380V 1500 rpm.

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In this paper we apply analytical and numerical conformal mapping (CM) to analyze magnetic field originating from permanent magnets and armature winding currents in a slotted air-gap of a surface mounted radial permanent-magnet synchronous motor (SPM) with taking into account the effect of arbitrarily curved motor surfaces. One of the conformal mappings used in this study, which is the numerical solution of the Schwarz-Christoffel (SC) integral, is obtained using Matlab SC Toolbox. We used this field solution to calculate cogging torque and electromagnetic torque by integrating the Maxwell stress tensor inside the air-gap. The accuracy of the developed method is verified by comparing its results with those obtained from the developed numerical finite-element (FE) model.

International audience; A method that combines an artificial neural network (ANN) and the finite element method (FEM) is introduced for estimating the instantaneous electromotive force (EMF) of surface mounted permanent magnet motors. Using parallel supervised multilayer neural networks, the geometrical parameters of the motors are mapped to the phase EMF. The ANN is trained using the FEM data. The ANN model is then used in an iterative designing process to achieve high torque development capability with low magnet volume.

International audience; This paper presents the use of air-gap interface method in TSFE for coupling the stator and rotor fields. It is developed by the authors in the case of time harmonic finite elements (THFE) analysis of cage induction motor [1]. The movement of the rotor is considered with no remeshing and no constraints imposed between the time step and element size. This method offers the possibility to consider only the first or some significant space harmonic orders.

International audience; We present a new accurate node's numbering method for minimizing the profile of stiffness matrix for a Cholesky's solving of the triangular finite elements problems. This method is suitable for cylindrical structures such electrical rotating machines and is especially intended for taking into account of the movement with classical methods.

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The authors present a new accurate node's numbering method for minimizing the bandwidth and the profile of stiffness matrix for a Cholesky's solving of triangular finite element problems. This method is applied for renumbering the mesh nodes.

In this paper the authors show that it is possible to estimate the parameters and the state model of an induction motor by using only two sensors. A new approach to dynamical modelling and parameters estimation of an induction motor is developed. The proposed model is based on spiral vector theory. This leads to a compact mathematical model in complex space which depends on the variables and the parameters of only one single phase of stator and rotor. This is called phase segregation method. A new filter is also used in order to estimate the complex states, parameters and rotor speed of an induction motor. This filter is a variant of non linear Kalman filter in its complex form “Extended complex Kalman filter, ECKF”.

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The rotor circuit time constant is an important parameter for indirect field oriented control. Incorrect estimation of the rotor time constant also leads to incorrect flux angle calculations and can cause significant performance deterioration if no means for compensation or identification is applied. The magnetic saturation, the operating temperature and difficulties in using sensors for speed measurement are one of the sources of parameters variations, caused by non-linear nature of the magnetizing curve and the variations of the rotor resistance. The classical indirect field-oriented control is highly sensitive to the inductance values decrease when increasing the saturation level. To solve this problem, the extended Kalman Filter associated to the neural network (EKF-ANNs) trained off line algorithm are used to estimate the rotor resistance, the main inductance and the rotor speed. The proposed EKF-ANNs compensation has shown a good performance in both the transient and steady state operations even in the presence of noise, and also at either variable speed operation in the field-weakening region or in the load variation.

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International audience; The present article deals with the wind power-generating unit of a Hybrid Photovoltaic –Wind Renewable Energy System (HPVWRES). The dynamic flux model of the self-excited induction generator used in the wind power-generating unit is given. This model, known to account for magnetic saturation is validated by experimental results. Operations under variable wind speed and/or variable load are discussed.

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