Evaluation of Predictive Models for Reducing Wind Turbine Power Converter Failure Downtime for a Wind Farm Operator Using SCADA Data

Abstract. To facilitate the continued growth of offshore wind farm developments, operations and maintenance (O&M) costs, which are estimated at 30 % of the lifetime costs of wind farms must be reduced. This could be achieved by moving current maintenance strategies to a predictive strategy. Predictive strategies use the turbine monitoring data to determine component remaining useful lifetimes, predict failure windows or detect drops in performance and then provide an optimised maintenance plan. To enable these strategies in practice, failure prediction models must be developed, that are useable by the wind farm operator for key components. This work identifies that power converters are responsible for significant downtime at some wind farms and prediction of their failures could offer significant improvements in turbine availability. Through an analysis of their failure mechanisms, the signals required to detect failures in the power converters are identified and the insufficiencies in the SCADA data available to operators are highlighted. Several machine learning and deep learning models are trained on the SCADA to predict the power converter failures, and a novel scoring function is applied to evaluate their performance when applied to the operational decision-making context. Results suggest that implementing an artificial neural network failure prediction model offers approximately 40 % reduction in power converter maintenance costs compared to business as usual. Further improvements to these models will require the acquisition of high frequency monitoring data specific to the power electronics in the power converters. Applying predictive maintenance strategies will generate extra wind farm revenues, reduce the number of maintenance actions taken and facilitate the work of maintenance teams.