Advanced diagnosis of wind turbines
The objective of the work to be carried out by the ESR7 is to develop a high performance monitoring system to be used in wind turbines to provide an advanced diagnosis of its components health.
Maintenance strategies need to be put in place in order to reduce the Operation & Maintenance (O&M) costs associated to Wind Turbines (WTs). Current maintenance planning is not optimized; it is possible to make WT’s operation more efficient. Condition Monitoring Systems (CMS) are employed to improve WT availability and reduce O&M costs. CM is defined as the collection and interpretation of the relevant parameters of a certain component for the purpose of the identification of any changes from normal conditions and trends of the health of the component under study. CM is divided into three main steps: data acquisition, signal processing and feature extraction. In order to provide diagnostic and prognostic of the WT’s health, fault detection algorithms need to be applied after signal processing and feature extraction. Online fault detection/diagnosis systems (FDS) offer an opportunity for improved maintenance and failure prevention strategies. They allow for early warnings of mechanical and electrical defects to prevent major component failures and to keep side effects on other components low. In this way they can be used to estimate the state of degradation or remaining life of a specific component in WTs. Therefore replacement of parts that are still operational is avoided without the risk of unforeseen breakdown.
The high performance monitoring system to be developed by the ESR7 will target the induction generation of the WT. Electrical parameters will be gathered under different conditions of the generator. For that, programming using LabVIEW will be employed. The same programme will incorporate several signal processing techniques to extract the relevant features to be used for fault detection algorithms.
Tentative schedule: March – August 2017
Loughborough University (LBORO)
Tentative schedule: August 2016 – January 2017
Aritgao, E (2016) Advanced diagnosis of wind turbines, VI Jornadas doctorales de la Universidad de Castilla La Mancha, Castilla La Mancha, Spain. Download Poster
Estefania Artigao, Andres Honrubia-Escribano, Emilio Gomez-Lazaro (2017) Current Signature Analysis to Monitor DFIG Wind Turbine Generators: A Case Study, Renewable Energy (http://dx.doi.org/10.1016/j.renene.2017.06.016)
Full paper: http://hdl.handle.net/10578/14892
Host Institution: Universidad de Castilla La Mancha (UCLM)