Abstract
This paper presents a dynamic discrete-time Piece-
Wise Affine (PWA) model of a wind turbine for the optimal
active power control of a wind farm. The control objectives
include both the power reference tracking from the system
operator and the wind turbine mechanical load minimization.
Instead of partial linearization of the wind turbine model at
selected operating points, the nonlinearities of the wind turbine
model are represented by a piece-wise static function based on
the wind turbine system inputs and state variables. The
nonlinearity identification is based on the clustering-based
algorithm, which combines the clustering, linear identification
and pattern recognition techniques. The developed model,
consisting of 47 affine dynamics, is verified by the comparison
with a widely-used nonlinear wind turbine model. It can be used
as a predictive model for the Model Predictive Control (MPC) or
other advanced optimal control applications of a wind farm.
| Original language | English |
|---|---|
| Journal | IEEE Transactions on Sustainable Energy |
| Volume | 6 |
| Issue number | 3 |
| Pages (from-to) | 831-839 |
| Number of pages | 10 |
| ISSN | 1949-3029 |
| DOIs | |
| Publication status | Published - 2015 |
Bibliographical note
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- Clustering based identification
- Model predictive control (MPC)
- Piece wise affine (PWA) model
- Wind turbine