Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 322-343 |
| Seitenumfang | 22 |
| Fachzeitschrift | Mechanical Systems and Signal Processing |
| Jahrgang | 40 |
| Ausgabenummer | 1 |
| Publikationsstatus | Veröffentlicht - 28 Mai 2013 |
Abstract
The test field alpha ventus is the first operating German offshore parks for wind energy. Twelve Wind Energy Converters (WECs) of the 5 MW-class are installed, both, for commercial and research reasons. Due to upcoming mass production and uncertainties in loads and behaviour, monitoring the foundation of these structures was desired. Two goals addressed are the extraction of modal parameters for model validation and the estimation of condition parameters to allow a hypothesis of the system's state. In a first step the largedatabase is classified by Environmental and Operational Conditions (EOCs) through affinity propagation which is a new approach for Structural Health Monitoring (SHM) on wind turbines. Further, system identification through data driven stochastic subspace identification (SSI) is performed. A new, automated approach called triangulation-based extraction of modal parapeters (TEMP), using stability diagrams, is a key focus of the presented research. Finally, extraction of condition parameters for tower accelerations classified by EOCs, based on covariance driven SSI and Vector Auto-Regressive (VAR) Models, is performed for several observation periods from one to 16 weeks. These parameters and their distributions provide a base line for long term observations.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Steuerungs- und Systemtechnik
- Informatik (insg.)
- Signalverarbeitung
- Ingenieurwesen (insg.)
- Tief- und Ingenieurbau
- Ingenieurwesen (insg.)
- Luft- und Raumfahrttechnik
- Ingenieurwesen (insg.)
- Maschinenbau
- Informatik (insg.)
- Angewandte Informatik
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
in: Mechanical Systems and Signal Processing, Jahrgang 40, Nr. 1, 28.05.2013, S. 322-343.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Monitoring a 5 MW offshore wind energy converter
T2 - Condition parameters and triangulation based extraction of modal parameters
AU - Häckell, Moritz W.
AU - Rolfes, Raimund
PY - 2013/5/28
Y1 - 2013/5/28
N2 - The test field alpha ventus is the first operating German offshore parks for wind energy. Twelve Wind Energy Converters (WECs) of the 5 MW-class are installed, both, for commercial and research reasons. Due to upcoming mass production and uncertainties in loads and behaviour, monitoring the foundation of these structures was desired. Two goals addressed are the extraction of modal parameters for model validation and the estimation of condition parameters to allow a hypothesis of the system's state. In a first step the largedatabase is classified by Environmental and Operational Conditions (EOCs) through affinity propagation which is a new approach for Structural Health Monitoring (SHM) on wind turbines. Further, system identification through data driven stochastic subspace identification (SSI) is performed. A new, automated approach called triangulation-based extraction of modal parapeters (TEMP), using stability diagrams, is a key focus of the presented research. Finally, extraction of condition parameters for tower accelerations classified by EOCs, based on covariance driven SSI and Vector Auto-Regressive (VAR) Models, is performed for several observation periods from one to 16 weeks. These parameters and their distributions provide a base line for long term observations.
AB - The test field alpha ventus is the first operating German offshore parks for wind energy. Twelve Wind Energy Converters (WECs) of the 5 MW-class are installed, both, for commercial and research reasons. Due to upcoming mass production and uncertainties in loads and behaviour, monitoring the foundation of these structures was desired. Two goals addressed are the extraction of modal parameters for model validation and the estimation of condition parameters to allow a hypothesis of the system's state. In a first step the largedatabase is classified by Environmental and Operational Conditions (EOCs) through affinity propagation which is a new approach for Structural Health Monitoring (SHM) on wind turbines. Further, system identification through data driven stochastic subspace identification (SSI) is performed. A new, automated approach called triangulation-based extraction of modal parapeters (TEMP), using stability diagrams, is a key focus of the presented research. Finally, extraction of condition parameters for tower accelerations classified by EOCs, based on covariance driven SSI and Vector Auto-Regressive (VAR) Models, is performed for several observation periods from one to 16 weeks. These parameters and their distributions provide a base line for long term observations.
KW - Affinity propagation
KW - Offshore wind turbines
KW - Operational modal analysis
KW - Stochastic subspace identification
KW - Structural health monitoring
KW - Vector Auto-Regressive models
UR - http://www.scopus.com/inward/record.url?scp=84881314412&partnerID=8YFLogxK
U2 - 10.1016/j.ymssp.2013.04.004
DO - 10.1016/j.ymssp.2013.04.004
M3 - Article
AN - SCOPUS:84881314412
VL - 40
SP - 322
EP - 343
JO - Mechanical Systems and Signal Processing
JF - Mechanical Systems and Signal Processing
SN - 0888-3270
IS - 1
ER -