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Accurate estimation of power system dynamics is very important for theenhancement of power system reliability, resilience, security, and stability ofpower system. With the increasing integration of inverter-based distributedenergy resources, the knowledge of power system dynamics has become morenecessary and critical than ever before for proper control and operation of thepower system. Although recent advancement of measurement devices and thetransmission technologies have reduced the measurement and transmission errorsignificantly, these measurements are still not completely free from themeasurement noises. Therefore, the noisy measurements need to be filtered toobtain the accurate power system operating dynamics. In this work, the powersystem dynamic states are estimated using extended Kalman filter (EKF) andunscented Kalman filter (UKF). We have performed case studies on WesternElectricity Coordinating Council (WECC)'s $3$-machine $9$-bus system and NewEngland $10$-machine $39$-bus. The results show that the UKF and EKF canaccurately estimate the power system dynamics. The comparative performance ofEKF and UKF for the tested case is also provided. Other Kalman filteringtechniques alongwith the machine learning-based estimator will be updatedinthis report soon.All the sources code including Newton Raphsonpower flow,admittance matrix calculation, EKF calculation, andUKF calculation are publiclyavailable in Github

Power System Dynamic State Estimation Using Extended and Unscented Kalman Filters