Improving wind turbine reliability through the analysis of critical component failures, parametric modelling, and maintenance optimization

Abstract

This study conducts a reliability analysis of a wind turbine system, focusing on shaft, gearbox, and generator. The goal is to evaluate their time between failures and determine the most suitable statistical models to describe their failure behaviours. Four distributions (3-parameter Weibull, 2-parameter exponential, normal, and 3-parameter lognormal) were assessed using maximum likelihood and least squares estimation with goodness-of-fit tests including Anderson-Darling (ADC) and Pearson correlation coefficients (PCC). Results show that gearbox is best modelled by 3-parameter lognormal, while generator fits the 3-parameter Weibull. The shaft yielded contradictory outcomes between ADC and PCC, which were resolved through Akaike and Bayesian information criterions implemented in Python, confirming the normal distribution as optimal. Reliability differences were evident with gearbox exhibited infant mortality and highest failure rates, shaft showed wear-out behaviour and generator demonstrated reliable and stable behaviour. These findings highlight the importance of component-specific modelling to optimize maintenance, reliability and system lifespan.