Robust Iteration-dependent Least Mean Square-based Distribution Static Compensator Using Optimized PI Gains

Abstract

A robust iteration-dependent least mean square (RIDLMS) algorithm-based fundamental extractor is developed to estimate the fundamental components of the load current for a four-wire DSTATCOM with a nonlinear load. The averaging parameter for calculating the variable step size is iteration dependent and uses variable tuning parameters. Rather than using the current value, the previous learning rate was used in this method to achieve a more adaptive solution. This additional control factor aids in determining the exact learning rate, resulting in reliable and convergent outcomes. Its faster convergence rate and the avoidance of local minima make it advantageous. The estimation of the PI controller gains is achieved through a self-adaptive multi-population algorithm. The adaptive change in the group number will increase exploration and exploitation. The self-adaptive nature of the algorithm was used to determine the subpopulation number needed according to the fitness value. The main advantage of this self-adaptive nature is the multi-population spread throughout the search space for a better optimal solution. The estimated gains of the PI controllers are used for the DC bus and AC terminal voltage error minimization. The RIDLMS-based control with PI gains obtained using the proposed optimization algorithm showed better power quality performance. The considered RIDLMS-supported control was demonstrated experimentally using d-SPACE-1104.