Empirical Physics-Based Normal Behavior Modeling of Drive Train Temperatures With High-Resolution Wind Turbine Operating Data

The installed wind energy capacity increases every year. However, operation and maintenance costs still make up a considerable portion of the levelized costs of electricity. This costs can be greatly reduced by the application of suitable early fault detection methods. The supervisory control and data acquisition system of wind turbines is one key element, as nowadays it provides high resolution operating data. Such data can be used for the implementation of normal behavior models, one precursor for concise early fault detection methods. In this work, we implement a generally applicable and interpretable partly physics-based, partly empirical normal behavior model for drive train temperatures in wind turbines. The model achieves a coefficient of determination (Formula presented.) well above 0.9 for 12 of 16 available temperature signals. We also present the possibility of interpreting the model's underlying physical correlations for a selected temperature signal. In addition, we evaluate the influence of the input data's temporal resolution on the model quality. While models based on 10 s data perform slightly better, the results show that 10 min data are sufficient for modeling temperatures.