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Real-time Moving Horizon Estimation for Induction Motors

Author(s):

D. Frick
Conference/Journal:

Diploma/Master Thesis, SS 12
Abstract:

Electrical drives account for 25% of consumed electricity in the United States. Model predictive control (MPC) schemes can be used to signifi cantly improve energy efficiency by utilizing the given switching frequency optimally. However these control schemes are often limited by the quality of the available state estimates. In this Master's thesis we present a systematic model-based approach to speed sensorless estimation for the induction motor. We use moving horizon estimation (MHE), an optimization based scheme that yields excellent performance and can be used with aggressive controllers such as MPC. The past measurements within a given horizon are combined with an a priori estimate based on the induction motor model. The resulting optimization problem is then solved using sequential quadratic programming, a technique for solving non-linear optimization problems. We implement the proposed MHE formulation on an industrial DSP, demonstrating the fi rst implementation of MHE on an experimental setup with sampling frequencies in the order of kHz. The performance of MHE is assessed through simulation and experiments, and compared to state-of-the-art estimators such as the extended Kalman Fi lter (EKF). We show that MHE can be implemented efficiently and is fast enough for real-time application. We also argue that it has a larger range of operation and that we can expect improved performance compared to the EKF.

Year:

2012
Type of Publication:

(12)Diploma/Master Thesis
Supervisor:

A. Domahidi

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% Autogenerated BibTeX entry
@PhdThesis { Xxx:2012:IFA_4421
}
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