dc.contributor.author | Perera, Aravinda | |
dc.contributor.author | Nilsen, Roy | |
dc.date.accessioned | 2021-10-21T07:08:39Z | |
dc.date.available | 2021-10-21T07:08:39Z | |
dc.date.created | 2021-04-26T13:16:21Z | |
dc.date.issued | 2021 | |
dc.identifier.isbn | 978-1-7281-5673-6 | |
dc.identifier.uri | https://hdl.handle.net/11250/2824318 | |
dc.description.abstract | Online adaptation of temperature-sensitive motor parameters is of significance for the electric drives in reliability-critical applications. Recursive prediction error method (RPEM) is widely used for this purpose. Gauss-Newton Algorithm (GNA), a prediction-error-gradients based algorithm, is adopted in this paper to find RPEM-gains for the parameter identification. This paper first investigates the simultaneous identifiability of permanent magnet flux linkage ( Ψm), and stator resistance (Rs) of interior permanent magnet synchronous machine (IPMSM) using both nonlinear observability theorem and RPEM. Subsequently, GNA is analyzed for its tracking capability, speed of convergence, need of gain-scheduling and computational demand in comparison to stochastic gradient (SGA), another algorithm of the same class, using steady and dynamic state simulations. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
dc.relation.ispartof | 2020 IEEE International Conference on Power Electronics Drives and Energy Systems - PEDES | |
dc.title | Gauss-Newton: A prediction-error-gradient based algorithm to track PMSM parameters online | en_US |
dc.type | Chapter | en_US |
dc.description.version | acceptedVersion | en_US |
dc.rights.holder | © IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
dc.identifier.doi | 10.1109/PEDES49360.2020.9379424 | |
dc.identifier.cristin | 1906426 | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |