Paper Info
Title
Development of a Black-Box Two-Level IGBT Three-Phase Inverter Compensation Scheme for Electrical Drives
Abstract
The control performance of three-phase electrical drives highly depends on the accuracy of the set voltage compared to its reference value, especially if the voltage is an input variable of integrated observers (e.g. flux observer). In general, voltage errors lead to a disturbance and, therefore, to a less-than-ideal control performance of the drive. Additionally, they increase harmonic currents within the motor and, hence, reduce its efficiency. Summarizing, there is a need for suitable inverter compensation schemes. Since it is difficult to generate and to parametrize a white-box inverter model, which precisely describes the inverter nonlinearity, in this paper, a black-box approach which utilizes machine learning is presented. Thereby, suitable neural network (NN) structures are selected in order to develop an appropriate inverter compensation scheme. Experimental results validate that trained neural networks can decrease the voltage errors by approx. 90 % compared to non-compensated control outputs and, at the same time, decrease the steady-state motor current harmonics significantly.
Year
DOI
Venue
2019
10.1109/ISIE.2019.8781543
2019 IEEE 28th International Symposium on Industrial Electronics (ISIE)
Keywords
DocType
ISSN
Motor control,inverter,machine learning,neural networks,system identification
Conference
2163-5137
ISBN
Citations 
PageRank 
978-1-7281-3667-7
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Marius Stender100.34
Oliver Wallscheid213.10
Joachim Böcker301.35