Model predictive and linear quadratic Gaussian control of a wind turbine

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5 Citations (Scopus)

Abstract

Model predictive and linear quadratic Gaussian controllers are designed for a 5MW variable-speed pitch-regulated wind turbine for three operating points – below rated wind speed, just above rated wind speed, and above rated wind speed. The controllers are designed based on two different linear dynamic models (at each operating point) of the same wind turbine to study the effect of utilising different control design models (i.e. the model used for designing a model-based controller) on the control performance. The performance of the LQG controller is enhanced by improving the robustness, achieved by replacing the Kalman filter with a modified Luenberger observer, whose gain is obtained to minimise the effect of uncertainty and disturbance.
LanguageEnglish
Number of pages24
JournalOptimal Control Applications and Methods
Early online date29 Feb 2016
DOIs
Publication statusE-pub ahead of print - 29 Feb 2016

Fingerprint

Wind Turbine
Wind turbines
Wind Speed
Controller
Controllers
Dynamic Linear Models
Robustness (control systems)
Control Design
Kalman filters
Model
Kalman Filter
Observer
Dynamic models
Disturbance
Model-based
Robustness
Minimise
Uncertainty

Keywords

  • wind turbine control
  • model predictive control
  • linear quadratic Gaussian
  • observer

Cite this

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title = "Model predictive and linear quadratic Gaussian control of a wind turbine",
abstract = "Model predictive and linear quadratic Gaussian controllers are designed for a 5MW variable-speed pitch-regulated wind turbine for three operating points – below rated wind speed, just above rated wind speed, and above rated wind speed. The controllers are designed based on two different linear dynamic models (at each operating point) of the same wind turbine to study the effect of utilising different control design models (i.e. the model used for designing a model-based controller) on the control performance. The performance of the LQG controller is enhanced by improving the robustness, achieved by replacing the Kalman filter with a modified Luenberger observer, whose gain is obtained to minimise the effect of uncertainty and disturbance.",
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