Torque ripple minimization in non-sinusoidal synchronous reluctance motors based on artificial neural networks

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 Vietna

A Self-Learning Solution for Torque Ripple Reduction for Non-Sinusoidal Permanent Magnet Motor Drives Based on Artificial Neural Networks

This paper presents an original method, based on artificial neural networks, to reduce the torque ripple in a permanent-magnet non-sinusoidal 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. The study 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. Either the torque or the speed control scheme, both integrate two neural blocks, one dedicated for optimal currents 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.CPER Région Alsace 2007-201

Robust multi-objective optimization of a photovoltaic system with grid connection

International audiencePhotovoltaic panels are a way of providing electricity without altering natural resources. Due to its intermittent nature and to meet consumers demand, this energy has to be stored, for example by using batteries. In this work, an energy model for buildings with battery is developed based on electrical consumption and production data. This model takes into account the depth of discharge, state of charge and efficiency over a cycle of a lithium type battery. Three rule-based strategies are then described. This leads to our optimization problem. The optimization is applied on two time slots: one day (in order to explore rapidly the behavior of different algorithm strategies) and one week (for pseudo real-time simulation). Robust multi-objective optimization is performed in order to reduce the impact of consumption prediction errors

Electrical–thermal modeling of a double-canned induction motor for electrical performance analysis and motor lifetime determination

International audienceIn nuclear and chemical fields, canned motor pumps are one of the safest ways to pump dangerous fluids. The canned motor pump is a compact unit integrating a hydraulic part and an induction motor with a common shaft. The particularity of the motor is that two non-magnetic cans are inserted in the air gap for leak tightness reasons. This study is therefore motivated by the fact that on one side classical electromagnetic and thermal models are not applicable for the canned motor, and on the other side new European standards about electrical machines require the design of motors with higher efficiency. In this work, an analytical model that combines electrical and thermal approaches has been performed to determine both electrical performances of the canned motor and the stator lifetime through the prediction of the maximum temperature of the winding for different operating conditions

Improved Electrical Modeling of a Double Canned Induction Motor with Squirrel Cage for Performance Analysis

International audienceThe use of canned motor pumps is one of the most secure ways to pump fluids in nuclear and chemical fields. Their particularity is the fact that two non-magnetic cans are introduced in the air gap for leak-tightness reasons. Therefore, the equivalent circuit of conventional induction motor is no more applicable. In this work, an analytical model that considers the effect of the cans for electrical performance analysis is performed. The input parameters are identified from specific tests and the model is validated by comparison with experimental data

Distortion improvement in the current coil of loudspeakers

International audienceThis paper deals with the comparison of voltage and current driving units in an active audio system. The effect of the audio amplifier control on the current coil of an electrodynamic loudspeaker is presented. In voltage control topology, the electromagnetic force linked to coil current is controlled through the load impedance. Thus, the electromechanical conversion linearity is decreased by the impedance variation, which implies a reduction of the overall audio quality. A current driving method could reduce the effect of the non-linear impedance by controlling the coil current directly, thereby the acceleration. Large signal impedance modeling is given in this paper to underline the non-linear effects of electrodynamic loudspeaker parameters on the coupling. As a result, the practical comparison of voltage and current driven methods proves that the current control reduces the voice coil current distortions in the three different loudspeakers under test