Data-Driven Linear Quadratic Control Scheme for Three-Phase Microgrid System Optimized by Genetic Algorithm

Abstract

This paper manifests Linear Quadratic (LQ) controller optimization techniques for a three-phase microgrid (MG) system. optimization has been done with the Genetic algorithm (GA). This paper demonstrates how data-driven optimization techniques can help designing an optimal controller. A comparative analysis has been drawn between the conventional proportional-integral-derivative (PID) controller scheme and the data-driven Linear Quadratic (LQ) control scheme. Two different LQ controller structures have been designed with GA optimization- Linear Quadratic Regulator (LQR) and Linear Quadratic Gaussian (LQG) controller. It becomes evident from the results that the optimized LQ controller outperforms the conventional PID controller. Between the two LQ control scheme, LQG has shown better performance in reference tracking and handling system transients. The system has been designed and optimized in the Matlab/ Simulink platform.