Adaptive Kalman Filter

Adaptive Kalman Filter

Betreuung:  Gaël Kermarrec
Jahr:  2021


Accurate estimation of the dynamic states of a synchronous machine (e.g., rotor’s angle and speed) is essential in monitoring and controlling transient stability of a power system. It is well known that the covariance matrixes of process noise (Q) and measurement noise (R) have a significant impact on the Kalman filter’s performance in estimating dynamic states. The conventional ad-hoc approaches for estimating the covariance matrixes are not adequate in achieving the best filtering performance. In this masterthesis, we want to investigate different methods for adaptive filtering for classical Kalman Filter and Extended Kalman Filter. Your tasks:

  • A strong bibliography describing the strengths and weaknesses of the different methods
  • An implementation and comparison of different methods (preference for Matlab)
  • We will work with simulated and real data.

One goal is the writing of a scientific paper (IEEE) to present the results.


You need a great sense of creativity and humor.