This paper presents a novel strategy of particle filtering for state estimation based on Generalized Gaussian distributions (GGDs). The proposed strategy is implemented with the Gaussian particle pilter (GPF), which has been proved to be a powerful approach for state estimation of nonlinear systems with high accuracy and low computational cost. In our investigations, the distribution which gives the complete statistical characterization of the given data is obtained by exponent parameter estimation for GGDs, which has been solved by many methods. Based on GGDs, an extension of GPF is proposed and the simulation results show that the extension of GPF has higher estimation accuracy and nearly equal computational cost compared with the GPF which is based on Gaussian distribution assumption.

The paper deals with the problems of designing observers and unknown input observers for discrete-time Lipschitz non-linear systems. In particular, with the use of the Lyapunov method, three different convergence criteria of the observer are developed. Based on the achieved results, three different design procedures are proposed. Then, it is shown how to extend the proposed approach to the systems with unknown inputs. The final part of the paper presents illustrative examples that confirm the effectiveness of the proposed techniques. The paper also presents a MATLAB® function that implements one of the design procedures.

Generalized observers are proposed to relax the existing conditions required to design Luenberger observers for rectangular linear descriptor systems with unknown inputs. The current work is focused on designing index one generalized observers, which can be naturally extended to higher indexes. Sufficient conditions in terms of system operators for the existence of generalized observers are given and proved. Orthogonal transformations are used to derive the results. A physical model is presented to show the usefulness of the proposed theory.

This paper investigates state estimation of linear time-invariant systems where the sensors and controllers are geographically separated and connected over limited capacity, additive white Gaussian noise (AWGN) communication channels. Such channels are viewed as dropout (erasure) channels. In particular, we consider the case with limited data rates, present a necessary and sufficient condition on the data rate for mean square observability over an AWGN channel. The system is mean square observable if the data rate of the channel is larger than the lower bound given. It is shown in our results that there exist the inherent tradeoffs among the limited data rate, dropout probability, and observability. An illustrative example is given to demonstrate the effectiveness of the proposed scheme.

An approach to power system state estimation using a particle filter has been proposed in the paper. Two problems have been taken into account during research, namely bad measurements data and a network structure modification with rapid changes of the state variables. For each case the modification of the algorithm has been proposed. It has also been observed that anti-zero bias modification has a very positive influence on the obtained results (few orders of magnitude, in comparison to the standard particle filter), and additional calculations are quite symbolic. In the second problem, used modification also improved estimation quality of the state variables. The obtained results have been compared to the extended Kalman filter method.

Both the growing number of dispersed generation plants and storage systems

and the new roles and functions on the demand side (e.g. demand side management) are

making the operation (monitoring and control) of electrical grids more complex, especially

in distribution. This paper demonstrates how to integrate phasor measurements so that

state estimation in a distribution grid profits optimally from the high accuracy of PMUs.

Different measurement configurations consisting of conventional and synchronous mea-

surement units, each with different fault tolerances for the quality of the calculated system

state achieved, are analyzed and compared. Weighted least squares (WLS) algorithms for

conventional, linear and hybrid state estimation provide the mathematical method used in

this paper. A case study of an 18-bus test grid with real measured PMU data from a 110 kV

distribution grid demonstrates the improving of the system’s state variable’s quality by

using synchrophasors. The increased requirements, which are the prerequisite for the use

of PMUs in the distribution grid, are identified by extensively analyzing the inaccuracy of

measurement and subsequently employed to weight the measured quantities.

Power system state estimation is a process of real-time online modeling of an electric power system. The estimation is performed with the application of a static model of the system and current measurements of electrical quantities that are encumbered with an error. Usually, a model of the estimated system is also encumbered with an uncertainty, especially power line resistances that depend on the temperature of conductors. At present, a considerable development of technologies for dynamic power line rating can be observed. Typically, devices for dynamic line rating are installed directly on the conductors and measure basic electric parameters such as the current and voltage as well as non-electric ones as the surface temperature of conductors, their expansion, stress or the conductor sag angle relative to the plumb line. The objective of this paper is to present a method for power system state estimation that uses temperature measurements of overhead line conductors as supplementary measurements that enhance the model quality and thereby the estimation accuracy. Power system state estimation is presented together with a method of using the temperature measurements of power line conductors for updating the static power system model in the state estimation process. The results obtained with that method have been analyzed based on the estimation calculations performed for an example system - with and without taking into account the conductor temperature measurements. The final part of the article includes conclusions and suggestions for the further research.

This paper presents a novel sideslip angle estimator based on the pseudo-multi-sensor fusion method. The

kinematics-based and dynamics-based sideslip angle estimators are designed for sideslip angle estimation.

Also, considering the influence of ill-conditioned matrix and model uncertainty, a novel sideslip angle estimator

is proposed based on the wheel speed coupling relationship using a modified recursive least squares

algorithm. In order to integrate the advantages of above three sideslip angle estimators, drawing lessons

from the multisensory information fusion technology, a novel thinking of sideslip angle estimator design is

presented through information fusion of pseudo-multi-sensors. Simulations and experiments were carried

out, and effectiveness of the proposed estimation method was verified.

The paper describes a novel online identification algorithm for a two-mass drive system. The multi-layer extended Kalman Filter (MKF) is proposed in the paper. The proposed estimator has two layers. In the first one, three single extended Kalman filters (EKF) are placed. In the second layer, based on the incoming signals from the first layer, the final states and parameters of the two-mass system are calculated. In the considered drive system, the stiffness coefficient of the elastic shaft and the time constant of the load machine is estimated. To improve the quality of estimated states, an additional system based on II types of fuzzy sets is proposed. The application of fuzzy MKF allows for a shorter identification time, as well as improves the accuracy of estimated parameters. The identified parameters of the two-mass system are used to calculate the coefficients of the implemented control structure. Theoretical considerations are supported by simulations and experimental tests.