Performance Analysis of Modified Shunt Active Line Conditioner (MSALC) Using Kalman Filter trained by Neural Network

Muralikrishnan gopalakrishnan,Nalin Kant Mohanty

In this paper, the power quality enhancer employed is a Modified Shunt Active line Conditioner (MSALC) using Kalman filter trained by neural network. The performance of the MSALC is tested both under balanced and unbalanced load conditions. Kalman filter is employed over here to propose a novel instruction current scheme to eliminate the usage of synchronous circuit and conventional PI controller resulting to efficient and economical MSALC design. The transient and dynamic power quality disturbances arising from the upstream and downstream of power distribution system which affects the dc-link voltage can be effectively selfregulated by the speedy and acclimated instruction current scheme. Kalman filter based novel instruction current generation scheme is proposed here in order to provide solutions to the problems arising during tuning for conventional PI controller. The Kalman filter is trained using neural network for superior performance under various load conditions. From the simulation results obtained using MATLAB/Simulink software, it is clear that the proposed control strategy for MSALC provides the enhanced performance in terms of accurate current harmonic elimination as well as power factor improvement both under steady state and dynamic load conditions, thus making it more economical.

Volume 11 | 02-Special Issue

Pages: 689-698