Abstract
This research aims to bring together thermal modelling and machine learning approaches to improve the understanding on the operation and fault detection of a wind turbine gearbox. Recent fault detection research has focused on machine learning, black box approaches. Although it can be successful, it provides no indication of the physical behaviour. In this paper, thermal network modelling was applied to two datasets using SCADA (Supervisory Control and Data Acquisition) temperature data, with the aim of detecting a fault one month before failure. A machine learning approach was used on the same data to compare the results to thermal modelling. The results found that thermal network modelling could successfully detect a fault in many of the turbines examined and was validated by the machine learning approach for one of the datasets. For that same dataset, it was found that combining the thermal model losses and the machine learning approach by using the modelled losses as a feature in the classifier resulted in the engineered feature becoming the most important feature in the classifier. It was also found that the results from thermal modelling had a significantly greater effect on successfully classifying the health of a turbine compared to temperature data. The other dataset gave less conclusive results, suggesting that the location of the fault and the temperature sensors could impact the fault-detection ability.
Highlights
Wind energy has an increasing share of the installed capacity of energy generation in the UK, in Europe and globally
The PFI analysis on dataset 2 does not give a clear indication of which variable has the greatest effect on classification and that there is a weak correlation between PFI and thermal modelling results
The results show an agreement between thermal modelling and PFI to an extent, but this agreement seems to be impacted by the failure mode examined and the distance of sensors from the failure
Summary
Wind energy has an increasing share of the installed capacity of energy generation in the UK, in Europe and globally. The projection of global power generation from wind energy is expected to continue to increase from around 3% in 2015 to 8% in 2030 [1]. This transition to low carbon energy generation is vital for sustainable development and to solve the climate crisis. To accelerate the transition to renewable, low carbon energy, the cost of wind energy generation must be reduced. WT gearbox condition monitoring and fault detection is important to improve reliability and to reduce the LCOE of wind
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