The electricity generation from wind has seen significant growth over the past two decades with a compound annual growth rate of over 21%, and this trend is projected to continue, aligning with the goals of the European Green Deal. In the context of offshore wind turbines, operational and maintenance costs can account for up to 30% of the total costs throughout the project's lifecycle. This highlights the need for the development of efficient condition monitoring methods aimed at mitigating maintenance expenses.
The gearbox is one of the most failure-prone components in wind turbines, leading to extended downtimes and substantial financial outlays. When the components of the gearbox degrade, the heat generated within the gearbox increases. This increase leads to higher oil temperatures, making the temperature signal a suitable indicator of the gearbox health condition. The main objective of this thesis is to design a physics-based normal behaviour model (NBM) for the estimation of the wind turbine gearbox oil temperature and to use field data to validate its effectiveness. The energy conservation principle is applied to the gearbox to formulate an equation for the calculation of the gearbox oil temperature. To account for some unidentified design specifications of the gearbox, the equation is fitted to available historical data from the Supervisory Control and Data Acquisition (SCADA) system to determine the unknown parameters. The model uses as input an array of operational measured signals available in the SCADA system, including rotor speed, power output, nacelle temperature and inlet oil temperature, which is the temperature of the oil after running through the cooling system.
A case study demonstrates the model's effectiveness in accurately predicting the gearbox oil temperature with a mean absolute percentage error of 0.75%. In order to investigate how well the physical characteristics of the gearbox are represented by the model, the coefficients of the energy balance equation derived from fitting it to the field SCADA data are compared to the parameters calculated using known characteristics of a reference gearbox. The comparison results indicate that the model's parameters are generally within the same order of magnitude as the reference values. To provide a benchmark, the performance of the physics-based model is also compared to that of two data-driven models using artificial neural networks (ANNs) for temperature prediction proposed in the literature. The results show that the proposed physics-based model outperforms both ANN models, achieving 14% and 59% lower root mean squared error (RMSE) and a reduction in time required for training of 70% and 95% when compared to the two ANN NBMs respectively.