### Abstract

Original language | English |
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Publication status | Published - 2010 |

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*Energy efficient control of an induction machine using a chaos particle swarm optimization algorithm*.

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**Energy efficient control of an induction machine using a chaos particle swarm optimization algorithm.** / Huynh, Duy; Dunnigan, Mathew Walter; Finney, Stephen.

Research output: Contribution to conference › Paper

TY - CONF

T1 - Energy efficient control of an induction machine using a chaos particle swarm optimization algorithm

AU - Huynh, Duy

AU - Dunnigan, Mathew Walter

AU - Finney, Stephen

PY - 2010

Y1 - 2010

N2 - This paper proposes a new application of a chaos particle swarm optimization (PSO) algorithm for loss model-based energy efficient control of an induction machine (IM) using an optimal rotor flux reference. The chaos PSO algorithm with a logistic map has been used for initializing a random value of the rotor flux reference, the inertia weight and two independent random sequences in the velocity update equation of the PSO algorithm. These result in the best convergence capability and search performance for the PSO algorithm in searching for an optimal rotor flux reference for energy efficient control of the IM. Additionally, this paper also proposes a recursive least-squares (RLS) algorithm with multiple time-varying forgetting factors for on-line parameter estimation of the IM. The estimated parameters are used to update IM parameter variations during operation. This means that the energy efficient control scheme is robust to parameter variations. Simulation results confirm the effectiveness of the proposed energy efficient control strategy.

AB - This paper proposes a new application of a chaos particle swarm optimization (PSO) algorithm for loss model-based energy efficient control of an induction machine (IM) using an optimal rotor flux reference. The chaos PSO algorithm with a logistic map has been used for initializing a random value of the rotor flux reference, the inertia weight and two independent random sequences in the velocity update equation of the PSO algorithm. These result in the best convergence capability and search performance for the PSO algorithm in searching for an optimal rotor flux reference for energy efficient control of the IM. Additionally, this paper also proposes a recursive least-squares (RLS) algorithm with multiple time-varying forgetting factors for on-line parameter estimation of the IM. The estimated parameters are used to update IM parameter variations during operation. This means that the energy efficient control scheme is robust to parameter variations. Simulation results confirm the effectiveness of the proposed energy efficient control strategy.

M3 - Paper

ER -