Convergence Parameter Analysis for Different Metaheuristic Methods Control Constant Estimation and it’s Tradeoff Inference

Abstract

This paper is an extension of our previous work, which discussed the difficulty in implementing different methods of resistance emulation techniques on the hardware due to its control constant estimation delay. In order to get rid of the delay this paper attempts to include the meta-heuristic methods for the control constants of the controller. To achieve the minimum Total Harmonic Disturbance (THD) in the AC side of the converter modern meta-heuristic methods are compared with the traditional methods. The convergence parameters, which are primary for the earlier estimation of the control constants, are compared with the measured parameters, tabulated and tradeoff inference is done among the methods. This kind of implementation does not need the mathematical model of the system under study for finding the control constants. The parameters considered for estimation are population size, maximum number of epochs, and global best solution of the control constants, best THD value and execution time. MatlabTM /Simulink based simulation is optimized with the M-file based optimization techniques like Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Cuckoo Search Algorithm, Gravity Search Algorithm, Harmony Search Algorithm and Bat Algorithm.