Genetic Algorithm Based Data-Driven ILQR Control Scheme for Three-Phase Microgrid System

This paper introduces an integrator augmented linear quadratic regulator (ILQR) control platform for improvedoperation of a three-phase microgrid (MG) system. The threephase MG system operated with LQR control scheme, often suffers from instability and poor performance during load shifting and the system transition period, while the proposed control scheme, ILQR, ensures stability and reduces the steadystate error of the system. The controller parameters have been tuned using a data-driven evolutionary optimization algorithm, genetic algorithm (GA). A comparison has been shown between the performance of GA optimized LQR and GA optimized ILQR control scheme. The proposed GA optimized ILQR scheme has shown significant improvement with 0% steadystate error while LQR has shown 3.15%. GA optimized ILQR has also ensured 3.1% overshoot, while LQR scheme made 10.4% overshoot. Matlab and Simulink platform have been used for simulations and optimization.