Risk-Based Decision Modelling for Wind Turbine Leading Edge Erosion

Abstract

IEA Wind Task 43 seeks to “unlock the full value of wind energy through digital transformation”. One mechanism to realize value is through enhanced data-driven decision-making and, while many areas in the wind sector can benefit from improved decision support, this case study focusses on a well-defined wind energy maintenance scenario involving blade inspection and repair. The solution concentrates on the specific damage category of blade leading edge erosion (LEE) and the optimum action to be taken for a given level of damage detected during periodic inspections. The key decision is whether to initiate repairs immediately or continue operating the turbine until the next inspection—and, if so, when that next inspection should take place. Even for such a specific damage type and decision option, the overall solution draws on multiple data types, ranging from damage classifications to cost drivers, and integrates a number of components including damage propagation, performance, and cost models. The core of the solution is a risk-based decision model using heuristic strategies, and Bayesian networks for optimized decision-making. This paper outlines the overall solution, expands on the data and modelling implementations, and discusses the results and conclusions arising from the investigation.