### Abstract

Original language | English |
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Pages | 444-449 |

Number of pages | 6 |

Publication status | Published - 2010 |

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### Cite this

*On-line parameter estimation of an induction machine using a recursive least-squares algorithm with multiple time-varying forgetting factors*. 444-449.

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**On-line parameter estimation of an induction machine using a recursive least-squares algorithm with multiple time-varying forgetting factors.** / Huynh, Duy; Dunnigan, Mathew Walter; Finney, Stephen.

Research output: Contribution to conference › Paper

TY - CONF

T1 - On-line parameter estimation of an induction machine using a recursive least-squares algorithm with multiple time-varying forgetting factors

AU - Huynh, Duy

AU - Dunnigan, Mathew Walter

AU - Finney, Stephen

PY - 2010

Y1 - 2010

N2 - This paper proposes a recursive least-squares (RLS) algorithm with multiple time-varying forgetting factors for on-line parameter estimation of an induction machine (IM). The regressive mathematical model of the IM is also introduced which is simple and appropriate for online parameter estimation. The estimator inputs using the proposed RLS algorithm are easily measurable variables such as the stator voltages and currents as well as the rotor speed of the IM. The simulation results obtained compare the estimated parameters with the IM parameters achieved using other RLS algorithms such as a standard RLS algorithm and a RLS with a constant forgetting factor. The comparison shows that the proposed RLS algorithm is better than others for on-line parameter estimation of the IM.

AB - This paper proposes a recursive least-squares (RLS) algorithm with multiple time-varying forgetting factors for on-line parameter estimation of an induction machine (IM). The regressive mathematical model of the IM is also introduced which is simple and appropriate for online parameter estimation. The estimator inputs using the proposed RLS algorithm are easily measurable variables such as the stator voltages and currents as well as the rotor speed of the IM. The simulation results obtained compare the estimated parameters with the IM parameters achieved using other RLS algorithms such as a standard RLS algorithm and a RLS with a constant forgetting factor. The comparison shows that the proposed RLS algorithm is better than others for on-line parameter estimation of the IM.

M3 - Paper

SP - 444

EP - 449

ER -