Bastian Ritter

Parametric model and identification of PMSM considering the influence of magnetic saturation

Parametric models for Permanent Magnet Synchronous Machines (PMSM) based on the magnetic coenergy function are presented. To characterise the nonlinear dependence on the stator currents, splines as piecewise polynomial functions are used. Also, a model of the utilised inverter considering error voltages due to the IGBT lock-time is shown. Two identification methods are described. Results of an identification of the parameters, extracted from measurement data, prove the validity of the models.

The design of nonlinear observers for wind turbine dynamic state and parameter estimation

This contribution addresses the dynamic state and parameter estimation problem which arises with more advanced wind turbine controllers. These control devices need precise information about the systems current state to outperform conventional industrial controllers effectively. First, the necessity of a profound scientific treatment on nonlinear observers for wind turbine application is highlighted. Secondly, the full estimation problem is introduced and the variety of nonlinear filters is discussed. Finally, a tailored observer architecture is proposed and estimation results of an illustrative application example from a complex simulation set-up are presented.

Adaptive Sigma-Point Kalman Filtering for Wind Turbine State and Process Noise Estimation

This contribution investigates different noise adaptive Kalman filtering techniques with regard to their usability for wind turbine application. Since advanced model-based control schemes arise as promising alternative for standard industrial control, the necessity for robust and adaptive state estimation techniques has simultaneously emerged as an important topic. The comparison of the implemented adaptation rules shows that the master-slave adaptive filters are very flexible, numerically efficient and easy to implement. Maximum likelihood estimation based methods are more robust but show less flexibility and fewer design parameters to influence the filter performance. The simulation study shows that adaptive filters are beneficial since they solve two typical problems involved with static Kalman filter design: First, filter parameter adaptation compensates incorrect assumptions of noise statistics. Secondly, adaptation rules prevent poor filter performance for systems with time-varying statistical properties.