Probabilistic analysis of wind turbine performance degradation due to blade erosion accounting for uncertainty of damage geometry

• Published in: Renewable & sustainable energy reviews 2023-05, Vol.178, p.113254, Article 113254
• Main Authors: Sergio Campobasso, M., Castorrini, Alessio, Ortolani, Andrea, Minisci, Edmondo
• Format: Article
• Language: English
• Subjects: Eroded blade aerodynamics; Leading edge erosion; Probability distribution function of turbine energy yield; Stochastic and deterministic methods for uncertainty propagation; Uncertain field records of blade erosion; Wind turbine predictive maintenance
• ISSN: 1364-0321; 1879-0690
• DOI: 10.1016/j.rser.2023.113254

Geometry alterations of wind turbine blades due to erosion reduce the blade aerodynamic performance, yielding turbine power and energy losses. This study proposes a novel probabilistic analysis framework combining computational fluid dynamics, probabilistic and deterministic uncertainty propagation, and high-performance computing to assess this performance degradation accounting for the unavoidable uncertainty on field records of blade erosion. This uncertainty presently prevents using erosion records for improving wind turbine maintenance planning, increasing energy yield, and thus further reducing the wind energy cost. The technology is demonstrated by quantifying the statistical moments of power and energy yield losses of an eroded utility-scale turbine at a North Sea offshore site and a southern European onshore site. The expectations of the offshore and onshore annual energy production losses are found to be 2 and 3% of the corresponding nominal values, respectively, with corresponding standard deviations of 0.1 and 0.15%. In the realistic scenario of erosion varying with high radial frequency, these low standard deviations result from partial compensation of the impact of mild and severe damages. These low standard deviations indicate that present uncertainty levels of erosion geometry records can be handled with uncertainty analysis in predictive maintenance for further reducing wind energy costs. With the frequent assumption of small or no radial variation of erosion, the standard deviation of the loss is misleadingly higher. For the first time, the study reports on the significant impact of turbulence intensity of the installation site on the turbine loss variability with the site wind characteristics. •Probabilistic analysis enables using blade erosion data for predictive maintenance.•Offshore AEP loss expectation and standard deviation are 2% and 0.1%, respectively.•Onshore higher turbulence intensity found to notably increase power and AEP losses.•Higher radial frequency of damage geometry lowers performance variability scatter.•Univariate reduced quadrature analysis is 30x+ faster than Monte Carlo analysis.