Marcin Lis

Tracking control of an inertial wheel pendulum by LQR regulation

This article describes universal tracking control method. It is based on model linearization in every point of trajectory. Tested device is an inertial wheel pendulum (IWP). This is an underactuated nonlinear object - two degrees of freedom (angle from vertical and angle of rotation of electric motor) and one actuator (current). The linear quadratic regulator (LQR) and the model of the object are analytically considered. Many experiments are presented and confirm proper and stable operation of the system and some constraints.

Dispersed filters for power system state estimation

The article proposes an approach to power system state estimation allowing the division of the network into smaller parts and performing calculations for each part at the same time. The latter can be implemented in parallel, but the main aim has been to propose a method for dispersed calculations, i.e. calculations that may be performed on computing units located at various points of the whole power system. In the paper, there are 3 algorithms for which dispersed versions have been proposed: Extended Kalman Filter, Particle Filter and Extended Kalman Particle Filter. As a result of the simulations, it has been verified that the Dispersed Particle Filter works better than simple Particle Filter. In two other cases, distributed algorithms work worse, but for the Extended Kalman Filter degradation in the estimation quality is not significant.

Particle Filter in State Vector Estimation Problem for Power System

Particle Filter is a tool, which has been used more frequently over the years. Calculations with using Particle Filter methods are very versatile (in comparison to the Kalman Filter), which can be used in high complex and nonlinear problems. Example of such a problem is the power system, where Particle Filter is used to state estimation of network parameters based on measurements. Paper presents theoretical basis regarding Par- ticle Filter and power system state estimation. Results of experi- ment have shown that Particle Filter usually gives better outcome comparing to the Weighted Least Squares method. In extension Multi Probability Density Function Particle Filter is proposed, which improves obtained results so that they are always better than Weighted Least Squares method.

Level of dispersion in dispersed particle filter

In the paper an impact of the calculations dispersion level in a power system on the estimation quality has been presented. The dispersion level has been changed from the smallest (calculations for the whole system) to the largest (individual calculations in each node of the system). The obtained results have been compared with the estimation quality of dispersed case of extended Kalman filter. Based on the performed simulation it has been concluded that the dispersion has positive influence on the estimation quality of dispersed particle filter method, but only to a certain level, i.e. in case of power system division into very small parts, unsatisfactory results have been obtained.