@inproceedings{ca408bcb00224b4ab1855242a00154a8,
title = "Encoderless self-commissioning and identification of synchronous reluctance machines at standstill",
abstract = "This paper presents an identification method and modelling technique which is able to characterise the complete nonlinear cross-coupling electromagnetic flux-linkage model of a synchronous reluctance machine as a function of the direct- and quadrature axes currents within a few seconds. The presented approach is suitable for identification of the self-saturation flux-curves as well as the cross-coupling flux-maps of synchronous machines without additional testing hardware. The proposed method is performed at standstill and is suitable for encoderless and self-commissioning applications. During the identification, the reference phase voltages and measurements of the phase currents are used to estimate the flux-linkages of the machine. Afterwards, the obtained data is utilised in a neural network training routine. The trained simple neural-network represents the complete flux-maps of the machine accurately, without discontinuities and with a small amount of model parameters which has been confirmed due to comparison of the results with the measurements of a constant speed method.",
keywords = "Electromagnetic Modelling, Encoderless, Flux-Linkage Maps, Machine Testing, Neural Network Machine Model, Self-Commissioning, Synchronous Machine",
author = "Simon Wiedemann and Kennel, {Ralph M.}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 26th IEEE International Symposium on Industrial Electronics, ISIE 2017 ; Conference date: 18-06-2017 Through 21-06-2017",
year = "2017",
month = aug,
day = "3",
doi = "10.1109/ISIE.2017.8001263",
language = "English",
series = "IEEE International Symposium on Industrial Electronics",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "296--302",
booktitle = "Proceedings - 2017 IEEE International Symposium on Industrial Electronics, ISIE 2017",
}