KALMAN FILTER BASED STATE ESTIMATION OF ISLANDED MICROGRID

Abstract

This report presents the importance of the state estimation of a microgrid. In section-1, current situation of power generation and power demand has been discussed. Also discussed about the Microgrid technology, importance of renewable energy sources in Microgrid technology and state estimation of Microgrid. In section-2, all the literature review has explained i.e., reasons for not using power system network modelling techniques in Microgrid and also discussed about the comparison between different types of state estimation techniques like Weighted least squares (WLS) method and Kalman filter. In section-3, classification of state estimation techniques and errors in state estimation has been discussed. WLS method and Kalman filter explained clearly. In WLS method, it requires computation of Jacobian matrix ‘H’ and inverse of information matrix in each iteration, making the model to be time extensive. Kalman filter gives the minimized error estimate even if noise is present in the system. The effectiveness of Kalman filter validated by estimating the states for freely falling body system. The variance of uncertainties is reducing in very few time steps. Finally in section-4, performed the state estimation for 4th order Islanded Microgrid system by using Kalman filtering and discussed about the numerical results. In this network it is observed that, the state error has mitigated for the system via Kalman Filter Estimation.