Model Predictive Control based Extended Kalman Filter to Improve Power Quality in Micro Grid with Improved Particle Swarm Optimized Selective Harmonic Elimination

N Narender Reddy, Jarupula Somlal, srujana

Distributed Generation (DG) units have some desirable features such as environment support, energy expansion, lower infrastructure costs and deregulation of energy market. DG, which is connected to the grid via point of common coupling, has many advantages such as peak shaving in order to reduce the overall cost of power by generating power during peak load hours, and also act as a standby generation to provide electrical power during outages Due to the availability of parallel connected UPS, increasing the number of DG’s is one of the feasible solutions to enhance the quality of power. In this paper we propose a novel Model Predictive Control Algorithm with Extended Kalman Filter (MPC-EKF) are employed for dynamic harmonic state estimation of hybrid DC micro grid to improve the power quality. The MPC control methodology decomposes the control object into steady-state and transient sub problems separately and enhances the transient and steady state responses which also identifies the harmonics present in the system with the help of EKF and after that an Improved Particle Swarm Optimized Selective harmonic Elimination (IPSO-SHE) is employed by adding adaptive inertia weight to reduce the harmonics and improve the power quality. Our proposed control system has been designed in MATLAB/SIMULINK and tested with different load conditions. The simulation results of the proposed control system and comparison with existing control systems shows the significance of the proposed work in terms of accuracy and faster computational time.

Volume 12 | Issue 2

Pages: 1272-1280

DOI: 10.5373/JARDCS/V12I2/S20201163