International audience

Modular Multilevel Converters (MMCs) are widely used in medium to high voltage/power applications such as in energy transformation and grid connected systems. MMCs benefit from being redundant and allowing transformer-less operations. Unfortunately, they suffer from having a high cost due to the high number of sub-modules (SMs). Moreover, they are hard to control. In order to operate the MMCs properly, the load current, the circulating current and the capacitor voltages should be controlled. The usual control methods are synthesized in order to control the average lower and upper arm capacitor voltage as well as the load current. However, in some special cases, the capacitor values could change due to the aging of the capacitors, or the SM may stop working completely. Thus, creating an imbalance in the capacitor voltages of each arm in the system. This paper presents a new control scheme that allows to control the voltage of the upper and lower arms separately while controlling the load current at the same time. The usual and the new proposed controllers are developed and compared through software simulation. Finally, the conclusion is drawn.

Squirrel cage induction motors are widely used in the industrial field as they present many advantages compared to other types of electric machines. They are self-starting, need little maintenance, can work in harsh environment and are relatively cheap. Many industrial applications requiring current or speed control of the machine need the knowledge of the machine parameters. Therefore, induction motor parameter identification algorithms are developed taking in consideration the accuracy of the identification process which impacts on the quality of the control and thereby the overall system. These algorithms are usually sensitive to measurement noise thus there usage in noisy industrial environment is not desirable. This paper presents three induction motor offline identification algorithms and test their accuracy and their ability to work in noisy environment. The first method subject to the study is a Least Mean Square (LMS) based identification method working at standstill, the second is a newly introduced method robust toward noises and the third is the classic no-load and blocked rotor identification method. Simulation results are presented and finally conclusions are drawn.

This paper is dedicated to control-based energy optimization tools useful for the electric vehicles (EVs) propulsion chain, which puts in interaction an electric motor and its energy source static converter. It is essential in energy optimization studies to build an accurate simulation model that takes into account all possible losses of the propulsion chain components according to the operating point. For that reason, the present work provides an accurate loss analysis solution, by studying the various elements losses of an electric traction chain motorized by an Electrically-Excited Synchronous Motor (EESM). The machine power balance is established while taking copper and iron losses into account. Moreover, the inverter and chopper losses are evaluated while using a model based on a novel technique that consists in creating a modified version of output voltages affected by the inverter and chopper losses. Finally, all losses data are gathered in order to simulate the system on-line losses distribution and global efficiency for different operating conditions.