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Offline and Online Parameter Estimation of Induction Machines: Advanced particle swarm optimization algorithms and advanced re,Used
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This book addresses offline and online 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 offline parameter estimation of the threephase and singlephase IMs. Additionally, a recursive leastsquares (RLS) algorithm with multiple timevarying forgetting factors is proposed for online 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 online parameter estimator using the RLS algorithm with multiple timevarying 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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