TU Darmstadt / ULB / TUprints

Identifiability and Observability Assessment for Nonlinear Wind Turbine Control Systems

Schmitt, Thomas (2017)
Identifiability and Observability Assessment for Nonlinear Wind Turbine Control Systems.
Technische Universität
Master Thesis, Primary publication

[img]
Preview
Updated version with License on Page 2. (Please use this one.) - Text
Masterthesis_TS_tuprints.pdf
Copyright Information: CC BY-NC 4.0 International - Creative Commons, Attribution NonCommercial.

Download (1MB) | Preview
Item Type: Master Thesis
Type of entry: Primary publication
Title: Identifiability and Observability Assessment for Nonlinear Wind Turbine Control Systems
Language: English
Referees: Konigorski, Prof. Dr. Ulrich ; Ritter, M. Sc. Bastian
Date: 30 November 2017
Place of Publication: Darmstadt
Date of oral examination: 19 December 2017
Abstract:

This work investigates the identifiability and observability of two nonlinear wind turbine models. Various definitions of both concepts are summarized together with an interpretation of their relation. An overview of existing methods to assess the identifiability and observability of nonlinear systems qualitatively as well as quantitatively is given. Of these, the profile likelihood approach is chosen and applied to both models. Thereby, statistical confidence intervals for parameters and states are derived. The identifiability of the air density, eigenfrequency and wind velocity as well as the observability of all states is assessed for various wind scenarios and measurement configurations. A qualitative overview is given together with a detailed analysis for selected constellations. Furthermore, the validity of the used methodology is verified.

URN: urn:nbn:de:tuda-tuprints-92643
Divisions: 18 Department of Electrical Engineering and Information Technology > Institut für Automatisierungstechnik und Mechatronik > Control Systems and Mechatronics
Date Deposited: 07 Nov 2019 11:54
Last Modified: 16 Jul 2020 09:08
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/9264
PPN: 455373825
Export:
Actions (login required)
View Item View Item