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For some industrial applications, experimental data is available in the form of several data sets corresponding to the operation of the plant under the same conditions. An example of such an application is the condition monitoring of a wind turbine based on SCADA data. Here, one is interested in the identiﬁcation of a turbine’ subsystem for a speciﬁc wind condition. However, long records of a given operating condition might be difﬁcult to obtain. Hence, one needs to select multiple short data-records from the operational data to identify the system. In this case, identiﬁcation approaches where missing data are treated as unknown parameters [1, 2] are not feasible due to the large amount of lost data. Then, the best option is to concatenate the data sets, and introduce additional parameters to handle the transient effects . Our aim is to verify the consistency of the estimates when considering this last approach. To this end, we performed a Montecarlo simulation to prove consistency when dealing with AR and ARX model structures.
|Titel||37th Benelux Meeting on Systems and Control|
|Status||Published - mrt 2018|
|Evenement||37th Benelux meeting on systems and control - Soesterberg, The Netherlands, Soesterberg, Netherlands|
Duur: 27 mrt 2018 → 29 mrt 2018
|Workshop||37th Benelux meeting on systems and control|
|Periode||27/03/18 → 29/03/18|
VingerafdrukDuik in de onderzoeksthema's van 'Identiﬁcation of LTI models from concatenated data sets'. Samen vormen ze een unieke vingerafdruk.
- 1 Talk or presentation at a conference