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

Abstract

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.