Analysing Current Signature Data to Diagnose an In-Service Wind Turbine Generator

Abstract

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.