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State and Parameter Estimation in LPV Systems Open Access


Other title
LPV Systems
State Estimation
Fault Detection
Parameter Estimation
Type of item
Degree grantor
University of Alberta
Author or creator
Wang, Ying
Supervisor and department
Zhao, Qing (Electrical and Computer Engineering)
Examining committee member and department
Prasad, Vinay (Chemical and Materials Engineering)
Chen, Tongwen (Electrical and Computer Engineering)
Zhao, Qing (Electrical and Computer Engineering)
Department of Electrical and Computer Engineering
Control Systems
Date accepted
Graduation date
Master of Science
Degree level
This thesis develops an online parameter and state estimation scheme for a linear parameter varying (LPV) system that utilizes an iterative moving window technique. In the proposed scheme, an online algorithm, based on the input/output measurement, is implemented to approximate the real system with the best match LPV model. The varying parameters in the LPV model can be estimated by solving a quadratic programming optimization problem, and state variable values can be calculated with an adaptive state observer. As an application, the wind turbine system is formulated as an LPV model and applied by the proposed scheme. In addition, the ranges of state and uncertainty are obtained in an online fault detection (FD) scheme, based on parity space models using a technique similar to the iterative moving estimation window. A two-level adaptive threshold for FD is designed to decrease the miss alarm rate based on the estimated ranges.
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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