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

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.