Abstract
A current development trend in wind energy is characterized by the installation of wind turbines (WT) with increasing rated power output. Higher towers and larger rotor diameters increase rated power leading to an intensification of the load situation on the drive train and the main gearbox. However, current main gearbox condition monitoring systems (CMS) do not record the 6‑degree of freedom (6-DOF) input loads to the transmission as it is too expensive. Therefore, this investigation aims to present an approach to develop and validate a low-cost virtual sensor for measuring the input loads of a WT main gearbox. A prototype of the virtual sensor system was developed in a virtual environment using a multi-body simulation (MBS) model of a WT drivetrain and artificial neural network (ANN) models. Simulated wind fields according to IEC 61400‑1 covering a variety of wind speeds were generated and applied to a MBS model of a Vestas V52 wind turbine. The turbine contains a high-speed drivetrain with 4‑points bearing suspension, a common drivetrain configuration. The simulation was used to generate time-series data of the target and input parameters for the virtual sensor algorithm, an ANN model. After the ANN was trained using the time-series data collected from the MBS, the developed virtual sensor algorithm was tested by comparing the estimated 6‑DOF transmission input loads from the ANN to the simulated 6‑DOF transmission input loads from the MBS. The results show high potential for virtual sensing 6‑DOF wind turbine transmission input loads using the presented method.
Highlights
The development in the field of wind energy has been characterized by the installation of new wind turbines with increasing rated power and the repowering of old turbines and wind farms [1,2,3]
Six feed-forward artificial neural network (ANN) models were trained to estimate each of the 6-degree of freedom (6-DOF) transmission input loads using the predictor parameters, listed in Table 1, as input
In addition to adjusting the number of nodes per layer, using the Rectified Linear Unit (ReLu) activation function and an Adaptive Moment Estimation (Adam) optimizer proved useful in increasing model accuracy as well as training speed
Summary
The development in the field of wind energy has been characterized by the installation of new wind turbines with increasing rated power and the repowering of old turbines and wind farms [1,2,3]. Higher towers and larger rotor diameters result in higher power as well as higher torsional loads on the WT drivetrain. The load situation within the drivetrain has a time-variant and unpredictable dynamic, which has a strong influence on the utilization of individual components. This results in deviations between the design load spectra and the actual load spectra occurring in operation [5, 6]. Such deviations could cause faster damage accumulations in drivetrain components, resulting in unexpected maintenance and increased downtime [5]
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