We propose a fully distributed Newton-type algorithm for state estimation of electric power systems. At each Newton iteration, matrix-splitting techniques are utilized to carry out the matrix inversion for calculating the Newton step in a distributed fashion. In order to reduce the communication burden as well as increase robustness of state estimation, the proposed distributed scheme relies on...

The objective of this paper is to control the speed of the motor using conventional controller; compensator is used to improve the steady state error. To evaluate the performance of the controller, time response analysis is carried out. The time response analysis consists of two type of analysis. One is unit step response analysis and other is performance indices analysis. The paper describes t...

This paper is concerned with the parameter estimation of a general class of nonlinear dynamic systems in state-space form. More speci cally, a Maximum Likelihood (ML) framework is employed and an Expectation Maximisation (EM) algorithm is derived to compute these ML estimates. The Expectation (E) step involves solving a nonlinear state estimation problem, where the smoothed estimates of the sta...

Reaction mechanism among indoline-2,3-dione, pyrrolidine-2-carboxylic acid and (Z)-2-(1-(2-hydroxynaphthalen-1-yl)ethylidene)hydroxycarboxamide to form 1’-((((aminooxy)carbonyl)amino)methyl)-2’-(1-hydroxynaphthalen-2-yl)-2’-methyl-1’,2’,5’,6’,7’,7a’-hexahydrospiro[indoline-3,3’-pyrrolo[1,2-a]imidazole-2-one was investigated using density functional theory (DFT) at B3LYP basis theory. The three-...

one of the most important economic factors is potential output. in macroeconomic models and structural studies, the estimation of potential output is necessary for projections and analyzing policy performances. there exist several methods for estimating potential output. meanwhile, its estimation is a difficult and complicated matter. empirical studies and researches show that using various tec...

A solution to the state estimation problem of systems with unmeasurable non-zero mean inputs/disturbances, which do not satisfy the disturbance decoupling conditions, is given using the Kalman filtering and Bayesian estimation theory. The proposed estimation algorithm, named Supervisory Kalman Filter (SKF), consists of a Kalman filter with an extra update step which is inspired by the particle ...