The application of reliable structural health monitoring (SHM) technologies to operational wind turbine blades is a challenging task, due to the uncertain nature of the environments they operate in. In this paper, a novel SHM methodology, which uses Gaussian Processes (GPs) is proposed. The methodology takes advantage of the fact that the blades on a turbine are nominally identical in structural properties and encounter the same environmental and operational variables (EOVs). The properties of interest are the first edgewise frequencies of the blades. The GPs are used to predict the edge frequencies of one blade given that of another, after these relationships between the pairs of blades have been learned when the blades are in a healthy state. In using this approach, the proposed SHM methodology is able to identify when the blades start behaving differently from one another over time. To validate the concept, the proposed SHM system is applied to real onshore wind turbine blade data, where some form of damage was known to have taken place. X-bar control chart analysis of the residual errors between the GP predictions and actual frequencies show that the system successfully identified early onset of damage as early as six months before it was identified and remedied.

Summary The influence of friction on wind turbines (WTs) is often seen as undesirable as it has been proven to be the root cause of different severe damages, where the most vulnerable WT's components are bearings. Accurate modelling and identification of friction affects are thus required in order to characterise frictional behaviour and prevent severe damages. This paper addresses the problem on the real‐time estimation of friction levels that vary in time with different rates in the WT's blade bearings. Because of ageing or damage progression in bearings, friction parameters change in time. Knowing the material properties and a damage model, monitoring of the friction can be used for condition monitoring of WT's blade bearings. The considered nonlinear identification methods suitable for estimation of fast varying parameters are the extended Kalman filter (EKF), the unscented Kalman filter, the particle filter (PF) and the PF combined with the EKF. This paper also proposes a novel modification of a differential evolution algorithm for the identification of parameters that slowly vary in time. The main contribution of this paper is the algorithm, which combines the modified differential evolution algorithm with optimal nonlinear filters for the estimation of parameters which vary at different rates. A LuGre friction model and a realistic WT sensor setup are selected. The techniques are benchmarked against simulated WT data, and their performances are compared and discussed. Copyright © 2015 John Wiley & Sons, Ltd.

No abstract

No abstract

Offshore wind power has been in the spotlight among renewable energy sources. The current trends of increased power ratings and longer blades come together with the aim to reduce energy costs by design optimisation. The standard approach to deal with uncertainties in wind‐turbine design has been by the use of characteristic values and safety factors. This paper focusses on modelling the effect of structural and aerodynamic uncertainties in blades. First, the uncertainties in laminate properties are characterised and propagated in a blade structural model by means of a Monte Carlo simulation. Wind tunnel measurement data are then used to define the variability in lift and drag coefficients for both clean and rough aerofoil behaviour, which is then used to extrapolate rough behaviours throughout the blade. A stochastic spatial interpolation parameter is used to define the evolution of the degradation level. The combined effect and the variance contribution of these two uncertainty sources in turbine loads is finally defined by aeroelastic turbine simulation. This research aims to provide a framework to deal with uncertainties in wind‐turbine blade design and understand their effects in turbine behaviour.