Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
A methodology for performance assessment at system level—Identification of operating regimes and anomaly detection in wind turbines
In the growing wind energy sector, as in other high investment sectors, the need to make assets profitable has put the spotlight on maintenance. Efficient solutions which leverage from condition or performance based maintenance policies have been proposed during the last decades, but the proposed methods generally focus on individual components or stand for specific application areas. This paper aims to contribute to the development of performance based maintenance strategies within the wind energy sector by providing a condition monitoring based generic methodology for wind turbine performance assessment at system level. The proposed methodology is based on the detection of critical periods in which low performance is detected repeatedly. Multiple machine learning methods and models are applied to assess the wind turbine performance. This methodology has been applied in a case study with SCADA data of eight wind turbines. An analyst could benefit from the implementation of the methodology and the easy-to-interpret results shown in the proposed control chart, especially in cases in which there is less know-how about which variables have higher impact on systems performance.
A methodology for performance assessment at system level—Identification of operating regimes and anomaly detection in wind turbines
In the growing wind energy sector, as in other high investment sectors, the need to make assets profitable has put the spotlight on maintenance. Efficient solutions which leverage from condition or performance based maintenance policies have been proposed during the last decades, but the proposed methods generally focus on individual components or stand for specific application areas. This paper aims to contribute to the development of performance based maintenance strategies within the wind energy sector by providing a condition monitoring based generic methodology for wind turbine performance assessment at system level. The proposed methodology is based on the detection of critical periods in which low performance is detected repeatedly. Multiple machine learning methods and models are applied to assess the wind turbine performance. This methodology has been applied in a case study with SCADA data of eight wind turbines. An analyst could benefit from the implementation of the methodology and the easy-to-interpret results shown in the proposed control chart, especially in cases in which there is less know-how about which variables have higher impact on systems performance.
A methodology for performance assessment at system level—Identification of operating regimes and anomaly detection in wind turbines
Urmeneta Olmedo, Jon (Autor:in) / Izquierdo, Juan (Autor:in) / Leturiondo, Urko
01.01.2023
Renewable Energy Volume 205, March 2023
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
DDC:
690
Reliable operating regimes of the rybinsk hydroelectric station turbines
| Online Contents | 1974
Reliable operating regimes of the rybinsk hydroelectric station turbines
| Springer Verlag | 1974
Transfer learning applications for autoencoder-based anomaly detection in wind turbines
| DOAJ | 2024
Structural impact assessment of low level jets over wind turbines
| American Institute of Physics | 2016