Nonlinear State Estimation and Noise Adaptive Kalman Filter Design for Wind Turbines
In order to obtain this valuable information without additional measurements and sensors, the present thesis bridges the scientific gap between the nonlinear estimation theory on the one hand and the practical application to wind turbine control systems on the other hand.
This approach includes the investigation of the nonlinear filter algorithms, the control-oriented physical models and the design methodology needed to make nonlinear state estimation techniques ready for wind turbine application. The results of this approach are so-called virtual (model-based) sensors that are employed for multiple estimation tasks, such as the observation of unknown disturbances and the online reconstruction of mechanical loads. These sensors are applicable whenever it is impossible or too expensive to measure the desired quantities directly.
This book provides the theoretical foundations, the practical application and also the simulative proof of concept in order to bring wind turbine state estimation successfully into practice. Thereby, these virtual sensors shall level the ground not only for advanced state-feedback control, but also provide further insight into the system’s internal behavior.