A Conditional Convolutional Autoencoder-Based Method for Monitoring Wind Turbine Blade Breakages

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

5 Scopus Citations
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Detail(s)

Original languageEnglish
Article number9146603
Pages (from-to)6390-6398
Journal / PublicationIEEE Transactions on Industrial Informatics
Volume17
Issue number9
Online published23 Jul 2020
Publication statusPublished - Sep 2021

Abstract

The wind turbine blade breakage is a catastrophic failure to a wind farm. Its earlier detection is critical to prevent the unscheduled downtime and loss of whole assets. This article presents a conditional convolutional autoencoder-based monitoring method, which is of twofold, for identifying wind turbine blade breakages. First, a novel conditional convolutional autoencoder taking a multivariate set of data as input is developed to derive reconstruction errors, which reflect changes of system dynamics caused by impending blade breakages. Next, a statistical process control principle is applied to develop boundaries for triggering blade breakage alarms based on reconstruction errors. The effectiveness of the conditional convolutional autoencoder-based method is validated with datasets collected by supervisory control and data acquisition systems installed in multiple commercial wind farms. We also demonstrate advantages of the conditional convolutional autoencoder-based monitoring method by benchmarking against the classical autoencoder and conditional autoencoder-based monitoring methods.

Research Area(s)

  • Condition monitoring, data mining, fault detection, neural networks (NNs), wind farm