Information about the states of a synchronous gen-
erator plays a crucial role in monitoring, controlling, and fault
detection and changes in the power system. However, due to
technical difficulties, this information is not always readily avail-
able from direct measurements. This paper proposes a method
for dynamic state estimation (DSE) using an Extended Kalman
Filter (EKF) approach based on linearization. The method aims
to estimate generator states using data measured at the generator
terminal. A comprehensive 7th-order sub-transient model is
employed to thoroughly depict the behavior of synchronous
generators in different system conditions, providing detailed
insights into how they react to faults or changes within the system.
The accuracy of estimated states is measured by comparing them
with actual synchronous generator states. The resilience of EKF
is comprehensively evaluated within a Single Machine Infinite
Bus (SMIB) system by considering various scenarios of changes
including short-circuit faults, and different levels of process and
measurement noises. The achieved results demonstrate the pro-
posed EKF approach’s ability to deliver precise state estimations
for the sub-transient synchronous generator model, relying solely
on terminal measurements. The achieved Mean Squared Error
(MSE) values range from a very low minimum of 3.80×10−6 to
a moderately low maximum of 3.06×10−2, further confirming
the EKF’s estimation accuracy.
