  Accurate estimation of power system dynamics is very important for the
enhancement of power system reliability, resilience, security, and stability of
power system. With the increasing integration of inverter-based distributed
energy resources, the knowledge of power system dynamics has become more
necessary and critical than ever before for proper control and operation of the
power system. Although recent advancement of measurement devices and the
transmission technologies have reduced the measurement and transmission error
significantly, these measurements are still not completely free from the
measurement noises. Therefore, the noisy measurements need to be filtered to
obtain the accurate power system operating dynamics. In this work, the power
system dynamic states are estimated using extended Kalman filter (EKF) and
unscented Kalman filter (UKF). We have performed case studies on Western
Electricity Coordinating Council (WECC)'s $3$-machine $9$-bus system and New
England $10$-machine $39$-bus. The results show that the UKF and EKF can
accurately estimate the power system dynamics. The comparative performance of
EKF and UKF for the tested case is also provided. Other Kalman filtering
techniques alongwith the machine learning-based estimator will be updated
inthis report soon.All the sources code including Newton Raphsonpower flow,
admittance matrix calculation, EKF calculation, andUKF calculation are publicly
available in Github
