State and Parameter Estimation in LPV Systems

• Author / Creator

  Wang, Ying

• 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.

• Subjects / Keywords

  • Parameter Estimation
  • Fault Detection
  • LPV Systems
  • State Estimation

• Graduation date

  Fall 2012

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/R3JD7M

• License

  This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.