Optimizing a Wind Turbine Oil Condition Monitoring Program

2012 ◽  
pp. 22-22-18
Author(s):  
Greg J. Livingstone ◽  
Jo Ameye ◽  
Dave Wooton
Keyword(s):  
Wind Turbine ◽  
Condition Monitoring ◽  
Monitoring Program ◽  
Turbine Oil ◽  
Oil Condition Monitoring

Optimizing a Wind Turbine Oil Condition Monitoring Program

2011 ◽  
Vol 8 (10) ◽  
pp. 103758 ◽  
Author(s):  
Greg J. Livingstone ◽  
Jo Ameye ◽  
Dave Wooton ◽  
A. Toms ◽  
S. W. Dean
Keyword(s):  
Wind Turbine ◽  
Condition Monitoring ◽  
Monitoring Program ◽  
Turbine Oil ◽  
Oil Condition Monitoring

scholarly journals Lubrication Oil Condition Monitoring and Remaining Useful Life Prediction with Particle Filtering

2020 ◽  
Vol 4 (3) ◽  
Author(s):  
Junda Zhu ◽  
Jae M. Yoon ◽  
David He ◽  
Yongzhi Qu ◽  
Eric Bechhoefer
Keyword(s):  
Dielectric Constant ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
Particle Filtering ◽  
Life Prediction ◽  
Remaining Useful Life ◽  
Oil Condition Monitoring ◽  
Lubrication Oil ◽  
Useful Life ◽  
Lubrication Oils

In order to reduce the costs of wind energy, it is necessary to improve the wind turbine availability and reduce the operational and maintenance costs. The reliability and availability of a functioning wind turbine depend largely on the protective properties of the lubrication oil for its drive train subassemblies such as the gearbox and means for lubrication oil condition monitoring and degradation detection. The wind industry currently uses lubrication oil analysis for detecting gearbox and bearing wear but cannot detect the functional failures of the lubrication oils. The main purpose of lubrication oil condition monitoring and degradation detection is to determine whether the oils have deteriorated to such a degree that they no longer fulfill their functions. This paper describes a research on developing online lubrication oil condition monitoring and remaining useful life prediction using particle filtering technique and commercially available online sensors. It first introduces the lubrication oil condition monitoring and degradation detection for wind turbines. Viscosity and dielectric constant are selected as the performance parameters to model the degradation of lubricants. In particular, the lubricant performance evaluation and remaining useful life prediction of degraded lubrication oil with viscosity and dielectric constant data using particle filtering are presented. A simulation study based on lab verified models is provided to demonstrate the effectiveness of the developed technique.

Cloud-connected real-time oil condition monitoring of utility transformers using impedance spectroscopy

2021 ◽  
pp. 1-12 ◽  
Author(s):  
Birhanu Alemayehu ◽  
Akash Kota ◽  
Amy Neidhard-Doll ◽  
Vamsy Chodavarapu ◽  
Guru Subramanyam
Keyword(s):  
Impedance Spectroscopy ◽  
Real Time ◽  
Condition Monitoring ◽  
Oil Condition Monitoring

scholarly journals Optimal Preventive Maintenance of Wind Turbine Components with Imperfect Continuous Condition Monitoring

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3801 ◽  
Author(s):  
Ahmed Raza ◽  
Vladimir Ulansky
Keyword(s):  
Wind Turbine ◽  
Condition Monitoring ◽  
Preventive Maintenance ◽  
False Negative ◽  
Maintenance Cost ◽  
Arbitrary Distribution ◽  
Average Lifetime ◽  
True Negative ◽  
Degradation Failure ◽  
Lifetime Maintenance

Among the different maintenance techniques applied to wind turbine (WT) components, online condition monitoring is probably the most promising technique. The maintenance models based on online condition monitoring have been examined in many studies. However, no study has considered preventive maintenance models with incorporated probabilities of correct and incorrect decisions made during continuous condition monitoring. This article presents a mathematical model of preventive maintenance, with imperfect continuous condition monitoring of the WT components. For the first time, the article introduces generalized expressions for calculating the interval probabilities of false positive, true positive, false negative, and true negative when continuously monitoring the condition of a WT component. Mathematical equations that allow for calculating the expected cost of maintenance per unit of time and the average lifetime maintenance cost are derived for an arbitrary distribution of time to degradation failure. A numerical example of WT blades maintenance illustrates that preventive maintenance with online condition monitoring reduces the average lifetime maintenance cost by 11.8 times, as compared to corrective maintenance, and by at least 4.2 and 2.6 times, compared with predetermined preventive maintenance for low and high crack initiation rates, respectively.

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