a b s t r a c tThis paper presents a general approach to predict the contact fatigue life of the gears in the drive-train system of a wind turbine under dynamic conditions. A simplified predictive pitting model that estimates service lives is presented and validated by comparisons with published experimental evidence. Finally, the predictive model is used to estimate the contact fatigue lives of the sun gear and planetary gears in the drive-train system of the National Renewable Energy Laboratory's 750 kW land-based wind turbine based on time domain simulations. The occurrence frequencies of different wind speeds are described by the generalized gamma distribution. The time series of the torques in the main shaft are obtained from a global dynamic response analysis of the wind turbine. The time series of the gear contact forces is obtained from a dynamic analysis of the gearbox using multi-body simulation. The two-parameter Weibull distribution, the three-parameter Weibull distribution, and the generalized-gamma distribution are used to fit the long-term probabilistic distribution of the gear tooth contact pressures. The case study shows the validity of the approach presented in this paper.
Abstract:The paper presents a detailed review of the state-of-the-art research activities on structural reliability analysis of wind turbines between the 1990s and 2017. We describe the reliability methods including the first-and second-order reliability methods and the simulation reliability methods and show the procedure for and application areas of structural reliability analysis of wind turbines. Further, we critically review the various structural reliability studies on rotor blades, bottom-fixed support structures, floating systems and mechanical and electrical components. Finally, future applications of structural reliability methods to wind turbine designs are discussed.
State of charge (SOC) and state of health (SOH) are two significant state parameters for the lithium ion batteries (LiBs). In obtaining these states, the capacity of the battery is an indispensable parameter that is hard to detect directly online. However, there is a strong correlation relationship between this parameter and battery internal resistance. This article first shows a simple and effective online internal resistance detection method. Secondly, the relationship between the measured internal resistance and the LiBs capacity is established by linear fitting. Finally, the capacity through internal resistance conversion is applied in SOC estimation. The estimation results show that this method can effectively enhance the SOC estimation accuracy regardless of temperature change and battery degradation.
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