Dynamic State Estimation in Power Systems Using Implicit Discretization Methods
State estimation is a key task that enables power systems operators to provide reliability and security for the power systems. Through the last decades, power systems have been undergoing significant expansion and complexification, which poses an array of challenges ahead for power systems operators. Various uncertainties in loads or generation are among the fundamental challenges that need to be addressed. Monitoring of the power network emerges as a key task that enables the system operator to apply reliable commands to control the network in case of an emergency. To accomplish this goal, there is a need to consider a comprehensive differential-algebraic model of power networks in dynamic state estimation. To this end, the present thesis proposes an optimization-based approach for solving the dynamic state estimation problem of power systems considering the implicit numerical discretization method. The method is capable of estimating the unknown transients and dynamic states of the system based on Phasor Measurement Unit (PMU) data. The performance and robustness of the developed approach are tested on IEEE-9 and IEEE-57 test networks considering different types of noises in measurements.