Open AccessAnalysing Current Signature Data to Diagnose an In-Service Wind Turbine Generator
Author(s) -
Estefanía Artigao,
Elena Gonzalez,
A. Honrubia-Escribano,
Emilio GómezLázaro
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1222/1/012042
Subject(s) - downtime , reliability engineering , reliability (semiconductor) , turbine , signature (topology) , wind power , computer science , doubly fed electric machine , induction generator , service (business) , generator (circuit theory) , condition monitoring , engineering , electrical engineering , ac power , power (physics) , voltage , mechanical engineering , physics , geometry , mathematics , economy , quantum mechanics , economics
Condition-based maintenance (CBM) is currently seen as the preferred approach to optimise operation and maintenance (O&M) strategies by the early detection and diagnosis of critical failures for wind turbines (WT). WT reliability is highly affected by failures of the mechanical components from the drive train, although doubly-fed induction generators (DFIG) also contribute to high failure rates and downtime. As DFIG is the dominant technology employed for variable speed WTs, dedicated failure detection and diagnosis techniques are required. Current signature analysis (CSA) has emerged as a powerful tool for this purpose, despite some limitations. In this work, the variability of CSA through time by means of spectra waterfall and the wavelet transform are investigated to overcome these limitations. The analysis of a faulty in-service WT DFIG presented in this work raises the usefulness of the suggested approaches to identify patterns related to failure development through time.