A Bayesian reliability analysis exploring the effect of scheduled maintenance on wind turbine time to failure

Abstract

This article presents a Bayesian reliability modelling approach for wind turbines that incorporates the effect of time‐dependent variables. Namely, the technique is used to explore the effect of annual services on wind turbine failure intensity through time for turbines within a currently operational wind farm. In the operator's experience, turbines seemed to fail more frequently after scheduled maintenance was performed; however, this is an unexplored effect in the literature. Additionally, the effects of seasonality, year of operation and position in the array on failure intensity are explored. These features were included in a Cox‐like model formulation which allows for time‐dependent covariates. Inference was performed via Bayes rule. Results show a spike in failure intensity reaching 1.57 times the baseline in the six days directly proceeding annual servicing, after which failure intensity is reduced compared to baseline. Also observed is a significant year‐on‐year reduction of failure intensity since the introduction of the site's data management system in 2018, a clear preference for modelling time to failure via a Weibull distribution and a dependence on location in the array with respect to the prominent wind direction. Results also show the benefit of employing a Bayesian regime, which provides easily interpretable uncertainty quantification.

On the Sensitivity of Wind Turbine Failure Rate Estimates to Failure Definitions

“…Third, a methodology could be employed to assess uncertainty both inherent in the data itself and in the calculated metrics. The current authors have employed Bayesian techniques to do so in the field of wind industry in previous studies [19,21]. Both of these studies could be improved by retroactively including the work procedure data upon which this study relies so heavily.…”