Application of Evolutionary Optimization Techniques for Optimal Location and Parameters Setting of Multiple UPFC Devices

Abstract

The unified power flow controller (UPFC) is one of the most promising flexible alternating current transmission systems (FACTS) devices for load flow control. UPFC can effectively control the load flow distribution, improve the usage of existing system installations, and improve the stabilities of the power network. However, the location of UPFC in the system plays a significant role in achieving such benefits. This paper deals with the application of two evolutionary optimization techniques, namely: genetic algorithm (GA) and particle swarm optimization (PSO) to find out the optimal number, the optimal locations, and the optimal parameters setting of multiple UPFCs devices. These variables are optimized to maximize the system loadability with minimum installation cost of UPFCs device. To show the validity of the applied techniques and for comparison purposes, we performed simulation on an IEEE 6-bus and an IEEE 14-bus test power systems. The results we've obtained show that UPFC can significantly increase the system loadability. Our results also indicate that both techniques can successfully find out the optimal location and the optimal parameters setting of multiple UPFCs.