Off-line and On-line Parameter Estimation of Induction Machines
Advanced particle swarm optimization algorithms and advanced recursive least-squares algorithms
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This book addresses off-line and on-line parameter estimations of an induction machine (IM) which are necessary to improve its control and operational performances. Two advanced particle swarm optimization (PSO) algorithms, known as the dynamic PSO and chaos PSO algorithms, are proposed for off-line parameter estimation of the three-phase and single-phase IMs. Additionally, a recursive least-squares (RLS) algorithm with multiple time-varying forgetting factors is proposed for on-line parameter estimation of the IM which can efficiently track the IM parameter variations during operation. Furthe...
This book addresses off-line and on-line parameter estimations of an induction machine (IM) which are necessary to improve its control and operational performances. Two advanced particle swarm optimization (PSO) algorithms, known as the dynamic PSO and chaos PSO algorithms, are proposed for off-line parameter estimation of the three-phase and single-phase IMs. Additionally, a recursive least-squares (RLS) algorithm with multiple time-varying forgetting factors is proposed for on-line parameter estimation of the IM which can efficiently track the IM parameter variations during operation. Furthermore, energy efficient control of the IM is also an important topic examined in this book. A control strategy is proposed using an optimal IM rotor flux reference. Two techniques, known as the derivative technique and the chaos PSO algorithm are proposed for obtaining the optimal IM rotor flux reference. The on-line parameter estimator using the RLS algorithm with multiple time-varying forgetting factors is used in this application to update the IM parameter variations so that the optimal IM rotor flux reference is always accurate and the IM efficiency always remains optimal.
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