Bayesian Approach to Non Linear State Estimation For Improved Performance of Smart Grid
Nisha Tayal1, Rintu Khanna2

1Nisha Tayal, Research Scholar, Assistant Professor, I.K. Gujral Punjab Technical University, Kapurthala, India, University of Engineering and Technology, Panjab University, (Chandigarh), India.
2Rintu Khanna, Professor, Punjab Engineering College, (Chandigarh), India.
Manuscript received on 18 June 2019 | Revised Manuscript received on 11 July 2019 | Manuscript Published on 17 July 2019 | PP: 932-938 | Volume-8 Issue-1C2 May 2019 | Retrieval Number: A11600581C219/2019©BEIESP
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Abstract: The process of PMU-based monitoring improves the quality of the smart grid. Simultaneously, the implementation of PMU increases the dynamics of noise variance which further inflates the uncertainty in noise-based distribution. This paper presents a method to reduce the amount of uncertainty in noise by using a linear quadratic estimation method (LQE), usually known as Kalman filter along with Taylor expansion series but this process is time-consuming and is vulnerable to a large number of errors at the time of testing. The main reason behind this approach is the high complexity of the system which makes it very hard to derive the process. The proposed studies adopts a technique to work on covariance earlier based estimation using Bayesian method together with the estimation of dynamic polynomial prior by using Particle Swarm Optimization (PSO). The experimental evaluation compares the outcomes received from the primary Kalman filter, PSO optimized Kalman filter out and Kalman filter Covariance Bayesian method. Finally, the effects received from the analysis highlights the truth that the PSO optimized Kalman clear out to be more effective than the Kalman filter out with Covariance Bayesian approach.
Keywords: Grid, PMU, Bayesian, Kalman, PSO, Parameter Estimation, Optimization.
Scope of the Article: Smart Grid Communications