[Poster Presentation]Long-Horizon Robust Direct Model Predictive Control for Medium-Voltage Drives with Active Neutral-Point Potential Balancing

IMPORTANT DATES

Digest Submission Opens: Oct. 30, 2022
Digest Submission Deadline: Mar. 20, 2023
Acceptance Notification: Apr. 28, 2023
Full Paper Submission Deadline: May. 20, 2023

Long-Horizon Robust Direct Model Predictive Control for Medium-Voltage Drives with Active Neutral-Point Potential Balancing

ID：76 View Protection：PUBLIC Updated Time：2023-06-12 17:31:22 Hits：693 Poster Presentation

Start Time：2023-06-19 09:00 （Asia/Shanghai）

Duration：0min

Session：[E] Poster Session » [E1] Poster Session 1

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Abstract

The paper presents a direct model predictive control algorithm for medium-voltage (MV) induction machines driven by three-level neutral-point-clamped (NPC) inverters that incorporates the neutral point potential balancing. For such nonlinear systems implementation of long horizons may be regarded as even a formidable task due to the high computational complexity. Nevertheless, this can be achieved with a modest calculation cost by decreasing the size of the underlying control optimization problem. Moreover, when assisted by a light estimation algorithm, the developed control scheme achieves a high level of robustness to variations in the motor parameters. The presented results demonstrate the effectiveness of the proposed method during steady-state and transient operating conditions.

Keywords

AC drives, medium-voltage (MV) drives, model predictive control (MPC), direct control, robust control

Speaker

Andrei Tregubov

Doctoral researcher Tampere University

Andrei Tregubov received the B.Sc. degree in electrical power engineering and M.Sc. degree in automation of energy systems from Peter the Great St. Petersburg Polytechnic University, Russia, in 2013 and 2015, respectively. In 2015 he received the M.Sc. diploma in electrical engineering/industrial electronics from Lappeenranta University of Technology, Finland, under the double degree program.
Since 2020 he has been working towards the Ph.D. degree in electrical engineering with the Faculty of Information Technology and Communication Sciences, Tampere University, Finland. His research interests include model predictive control of electrical drive systems, parameter estimation techniques, and the embedded implementation of control algorithms.

Petros Karamanakos

Associate professor Tampere University

Petros Karamanakos is an Associate Professor at the Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland. Dr. Karamanakos received the Diploma and the Ph.D. degrees in electrical and computer engineering from the National Technical University of Athens (NTUA), Athens, Greece, in 2007, and 2013, respectively. Prior to joining Tampere University, he was with the ABB Corporate Research Center, Baden-Dättwil, Switzerland, and the Chair of Electrical Drive Systems and Power Electronics, Technische Universität München, Munich, Germany. His main research interests lie at the intersection of optimal control and modulation, mathematical programming and power electronics, including model predictive control for utility-scale power converters and ac variable speed drives.

Ludovico Ortombina

Researcher University of Padua

Ludovico Ortombina received the M.Sc. and Ph.D. degrees in mechatronics engineering from the University of Padova, Italy, in 2015 and 2019, respectively. Since August 2020, he has been a Researcher with the Department of Industrial Engineering, University of Padova. His research interests include parameter estimation techniques for syn-
chronous motors, sensorless controls, and predictive control.

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