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The purpose of this paper is to present a model of the load of the wind turbine gears for simulation of real, varying operational conditions for modelling of wind turbine vibration. The characteristics of the wind, which generates chaotically varying loads on the drivetrain components generating load in teeth and bearings of gears during torque transfer, are discussed. A generator of variable load of wind turbines drivetrain is proposed. Firstly, the module for generation of wind speed is designed. It is based on the approach in which the wind speed was considered as a time series approximated by the Weierstrass function. Secondly, the rotational speed of the main shaft is proposed as a function of the wind speed value. The function depends on a few parameters that are fitted by using a genetic algorithm. Finally, the model of torque of the main shaft is introduced. This model has been created by using a multi-layer artificial neural network. The results show that the proposed approach yields a very good fit for the experimental data. The fit brings about the proper reproducing of all the aspects of the load that are crucial for causing fatigue and, as a consequence, damaging of gears of the wind turbines. [ABSTRACT FROM AUTHOR]

Abstract: In this paper a wind speed model as a time series is presented. The main goal is to use it as a model of the load of wind turbine gears for simulation of real, varying operational conditions for wind turbine vibration modelling. Several wind characteristics for evaluation of the wind model accuracy in the context of a wind turbine gears load simulation are proposed. Verification of the hypothesis that wind speed as a time series has a fractal character is another aim of this paper. The Weierstrass functions family, whose graphs are fractal sets, in combination with medium-term trends, is used as the basis of the model. The function with a fractal component is fitted to the past observation data. A genetic algorithm is used as the fitting procedure. Fractal dimension is utilized as a parameter in a fitness function. The results have shown that the proposed approach yielded very good fit for the observation data. The hypothesis about fractal character of the wind speed is positively verified. [Copyright &y& Elsevier]