Improved Parameter Estimation of Smart Grid by Hybridization of Kalman Filter with Bayesian Approach

Abstract

The smart grid technology represents an evolutionary change in the process of transforming the energy from one state to another providing an exceptional smart opportunity to monitor and enhance the reliability, availability, and efficiency of the energy-based industry. Smart grids offer a platform which provides an efficient way of transmission, quicker-restoration, reduces the operational cost and peak demand of a system, thereby resulting in better integration and improved form of security. The phasor measurement unit (PMU) plays a very significant role in smart grid technology, where it contributes to measuring the synchro phasors, thus making it valuable to dynamically monitor different types of transient processes occurring in a system. The parameter estimation of PMU measures the frequency change by taking the difference of two successive frequencies that may worsen the quality of noise. However, in this paper, proposed work is done on PMU-parameter estimation by using an extended version of the Kalman filter along with the optimization techniques. The proposed algorithm of the Kalman filter used in the process helps in predicting the states of noise and covariance. Further, the optimization of the generated output is done using an intelligent PSO technique. The experimental analysis indicates that the results obtained from PSO-optimized Kalman filter are more effective than the considered existing approaches.