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An approved superiority of real-time induction machine parameter estimation operating in self-excited generating mode versus motoring mode using the linear RLS algorithm: Ideas & applications.
- Source :
-
International Journal of Electrical Power & Energy Systems . Jun2020, Vol. 118, pN.PAG-N.PAG. 1p. - Publication Year :
- 2020
-
Abstract
- • An approved method based on the RLS algorithm is presented. • Comparison between the standard RLS algorithm and the proposed method. • The proposed method performances are confirmed by experiment tests. This paper describes another way to perform the on-line induction machine's parameter identification using the standard linear form of the recursive least-squares algorithm (RLS), which is commonly applied to estimate the machine parameters in motoring mode using an appropriate linear form machine model. The proposed approach shows that with this same methodology, better results could be obtained when the considered machine is running in self-excited generating mode. The on-line parameter estimation task is performed just during the beginning of the voltage build-up process where the magnetizing machine curve is linear and both saturated and non-saturated machine models present the same dynamic behavior. Since the rotor is driven at a constant speed and the required reactive power is provided by a capacitors bank connected to the stator windings terminals, this fact allows a direct application of the standard RLS estimation algorithm to the frequently linear machine model without any voltage measurements and without any additional restrictions generally imposed by the motoring mode. These potential advantages will contribute to significantly reduce the estimation algorithm complexity. Simulation results validated by experimental tests confirm the effectiveness of the proposed approach with improved accuracy. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01420615
- Volume :
- 118
- Database :
- Academic Search Index
- Journal :
- International Journal of Electrical Power & Energy Systems
- Publication Type :
- Academic Journal
- Accession number :
- 141582096
- Full Text :
- https://doi.org/10.1016/j.ijepes.2019.105725