Current environmental and economic trends have forced grid operators to maximize the utilization of the existing assets, which is causing systems to be operated closer to their stability limits than ever before. This requires, among other things, better knowledge and modelling of the existing power system equipment to increase the accuracy of the assessment of current stability margins.This research investigates the possibility of improving the quality of load modeling. The thesis presents a review of the traditional methods for estimation of load model parameters and proposes to use Improved Particle Swarm Optimization. Different algorithms are tested and compared in terms of accuracy, reliability and CPU requirements using computer simulations and real-data captured in a power system.Estimation of frequency and power components has also been studied in this thesis. A review of the existing methods has been provided and the use of an Unscented Kalman Filter proposed. This nonlinear recursive algorithm has been thoroughly tested and compared against selected traditional techniques in a number of experiments involving computer-generated signals as well as measurements obtained in laboratory conditions.