State estimation is an important power system application that is used to estimate the state of the power transmission networks using (usually) a redundant set of sensor measurements and network topology information. Many power system applications such as contingency analysis rely on the output of the state estimator. Until recently it was assumed that the techniques used to detect and identify...

Nonlinear Bayesian filtering has been utilized in numerous fields and applications. One of the most popular class of Bayesian algorithms is Particle Filters. Their main benefit is the ability to estimate complex posterior density of the state space in nonlinear models. This paper presents the application of particle filtering to the problem of parameter estimation and calibration of a nonlinear...

In this paper an unscented Kalman filter with guaranteed positive semidefinite state covariance is proposed by calculating the nearest symmetric positive definite matrix in Frobenius norm and is applied to power system dynamic state estimation. The proposed method is tested on NPCC 48-machine 140-bus system and the results validate its effectiveness.

this paper presents a new control methodology for active power filters that provide an adaptive online harmonic estimation with partial and selective harmonic reduction schemes, which has been implemented within an integrated controller. the proposed approach is to provide partial and selective reduction of those individual harmonics which exceed the recommended levels as set by regulatory bodi...

This paper summarizes a review of the distribution system state estimation (DSSE) methods, techniques, and their applications in power systems. In recent years, implementation distributed generation has affected behavior networks. order to improve performance networks, it is necessary implement methods. As transmission networks are not similar due variations line parameters, buses, measuring in...