Wind Turbine Recovery Forecasting using Survival Analysis

Abstract: The goal of this thesis is to present a methodology for predicting time until recovery of failed wind turbines. The necessity is motivated by the potential for more accurate wind energy export forecasts. The current approach rests entirely on having an expert examine the turbine and produce a time estimate. Due to its nature, such a prediction cannot be made immediately upon failure. Five common survival analysis models are evaluated in regard to their ability to correctly classify recovery as happening within or after 24 hours of failure, and point prediction error in the case that the failure event is predicted to resolve within 24 hours. A method for nonparametric clustering of survival curves is developed, that is used to reduce the number of variables in the examined models. The Weibull Accelerated Failure Time model with the clustered error codes and logarithm of energy produced in the month prior to failure is found to perform significantly better than alternatives. Classification is optimized by finding optimal thresholds using ROC curves. An attempt is made to present theory necessary to motivate the models used.