Abstract
The paper presents a new stability analysis approach applicable to wind turbines. At first, a reduced order periodic model is identified from response time histories, and then stability is assessed using Floquet theory. The innovation of the proposed approach is in the ability of the algorithm to simultaneously consider multiple response time histories, for example in the form of measurements recorded both on the rotor and in the stand still system. As each different measurement carries a different informational content on the system, the simultaneous use of all available signals improves the quality and robustness of the analysis.
Highlights
The linear time periodic approximation for the stability analysis of wind turbines is amply understood and adopted
Review of Floquet theory for discrete time systems we briefly review Floquet theory, while for an in depth explanation the reader is referred to Refs. [6, 7]
One may define an output matrix CMBC(k) that provides as principal harmonic the one that would have been obtained by the Multi Blade Coordinate (MBC) LTI approximation
Summary
The linear time periodic approximation for the stability analysis of wind turbines is amply understood and adopted. Numerical methods as the implicit Floquet theory are able to lower its computational cost, but they require a linearization of the system For this reason some authors preferred to resort to a system identification approach, which has the advantage of being applicable to field data. [1], the authors conducted the stability analysis of a wind turbine by using a SISO (single input, single output) Periodic ARX (autoregressive exogenous). This identification method successfully accomplished the task. [2] the authors presented a subspace identification method and used it to identify the periodic modes of a simple helicopter model. They were scaled with respect to their maximum absolute values, in order to improve the problem conditioning
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