Blade Bearing Friction Estimation of Operating Wind Turbines

Perišić, Nevena; Pedersen, B. J.; Kirkegaard, Poul Henning

doi:10.1007/978-1-4614-2419-2_19

Cited by 3 publications

(5 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to make realistic assumptions before approaching the identification problem, it is necessary to know which measurements are available on the operating WT. The signals present in the global pitch system model are the following: M B The resulting blade root blade moment. The edwise and flapwise moments on the blade can be measured either using strain gauge sensors or optical fibres.…”

Section: Modelling Of the Pitch Systemmentioning

confidence: 99%

Section: Sensors Availabilitymentioning

confidence: 99%

“…This model is based on the assumption that the bearing friction moment is due to Coulomb friction and that it depends on the resulting blade root bending moment M B , axial load F A , radial load F R , unloaded friction torque M 0 and Coulomb friction damping F c . The most significant influence is from the M B , around 90%, .…”

Section: Modelling Of the Pitch Systemmentioning

confidence: 99%

“…The nonlinear filters benchmarked in this work, suitable for the fast varying states, are the EKF, the UKF, the particle filter (PF) and the PF combined with the EKF. A novel modification of differential evolution (M‐DE) algorithm is proposed and tested for the estimation of slowly varying friction parameters.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Friction estimation in wind turbine blade bearings

Stevanović

Green

Worden

et al. 2015

Struct. Control Health Monit.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Modelling Of the Pitch Systemmentioning

confidence: 99%

Section: Sensors Availabilitymentioning

confidence: 99%

Section: Modelling Of the Pitch Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Friction estimation in wind turbine blade bearings

Stevanović

Green

Worden

et al. 2015

Struct. Control Health Monit.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Recent research show that a linearly increasing friction coefficient could be detected, given that reliable measurements of PM torque and blade moments are available, as well as a reliable model for the PM torque including a bearing friction model. 6 The PM torque is the most direct measure of the bearing friction, but measurement systems for PM torque and blade moments are expensive and need to be maintained, and are therefore not standard measurements for wind turbines. The PM current is known by the control system (PLC), and for some turbines, it is included in the SCADA data.…”

Section: Introductionmentioning

confidence: 99%

Analysis of pitch system data for condition monitoring

2013

View full text Add to dashboard Cite

Measurements of pitch motor torque and current give indirect information about the condition of the pitch system and can therefore potentially be used for condition‐based maintenance. This paper presents an analysis of these measurements for a wind turbine, and the measurements are compared with a theoretical model based on aeroelastic simulations. The blade moment is found to have only minor influence on the friction in the blade bearing. The main factors affecting the static friction are the temperature and time after the latest pitch movement. Pitch motor current and torque are proportional at a constant pitch velocity, but the 10 min maximum values are only approximately proportional, because the maximum values occur during acceleration and not simultaneously. These findings are important to consider, if using the pitch motor current or torque as an indicator for the pitch system health is considered. Copyright © 2013 John Wiley & Sons, Ltd.

show abstract

Modelling hydraulic pitch actuator for wind turbine simulation under healthy and faulty conditions ★ ★This work has been funded by the Walloon Region. The POWER project has been accredited in 2011 in the framework of the Marshall plan implemented by the competitiveness cluster on mechanical engineering called Mecatech.

2015

View full text Add to dashboard Cite

Blade Bearing Friction Estimation of Operating Wind Turbines

Cited by 3 publications

References 13 publications

Friction estimation in wind turbine blade bearings

Friction estimation in wind turbine blade bearings

Analysis of pitch system data for condition monitoring

Contact Info

Product

Resources

About