A novel control strategy for DVR: Optimal bi-objective structure emotional learning

Abstract

There are different approaches to provide high power quality for sensitive loads in fault conditions. In this research, “Dynamic Voltage Restorer (DVR)” is used to compensate the harmful effects of disturbances on voltage. In order to improve the performance of DVR’s controller from point of view of power quality indices, voltage sag and THD are considered as first and second object respectively so a new structure is suggested for this controller. The proposed controller is based on human brain learning which is a self-tuning PI controller that is called emotional controller. In that paper a bi-objective structure emotional learning is recommended. Also using this controller, system had better performance during fault conditions in term of both these power quality’s indices. Furthermore, considering voltage THD as second goal influence on major goal considerably which is voltage sag. Power systems sometimes have complicated dynamic behavior especially during faults. Most controllers have difficulty doing their best performance in these situations. Therefore, in order to modify the performance of this controller from point of view of mentioned power quality indices, we make a decision to regulate this controller’s parameters with an optimization algorithm. Self Adaptive Modified Bat Algorithm (SAMBA) is considered as a powerful optimization algorithm in this paper. This optimization algorithm is a modified version of Bat algorithm. The optimization algorithm can overcome to some problems which are common in other optimization algorithms. The algorithm has had a considerable performance rather than standard Bat and PSO algorithm. Also according to simulation results, this proposed method works significantly better than classic PI controller, bi-objective emotional controller and some intelligent controllers that have introduced in other researches already.