Abstract
In this paper we present a closed-loop LPV identification algorithm that uses a periodic scheduling sequence to identify the rotational dynamics of a wind turbine. In the algorithm we assume that the system undergoes the same time variation several times, which make it possible to use time-invariant identification methods since the input and output data are chosen from the same point in the variation of the system. We use closed-loop time-invariant techniques to identify a number of extended observability matrices and state sequences that are, inherent to subspace identification, identified in a different state basis. We show that by formulating an intersection problem all the states can be reconstructed in a general state basis from which the system matrices can be estimated. The novel algorithm is applied on a wind turbine model operating in closed loop.
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