Optimal Power Flow with FACTS Controller Using Hybrid PSO

Abstract

Optimal power flow (OPF) is to find steady state operation point which minimizes generation cost, loss etc. or maximizes loadability in power system, which improves the system performance by satisfying certain constraints. In general, different optimization methods are used in the literature to solve the OPF problem. In some research works, the optimization process is done by considering total fuel cost or by considering the environmental pollution that occurs during power generation. But in some other research works, FACTS controllers are used to improve the power flow without considering the power generation cost. Power loss is one of the most important parameters in OPF, but in most of the research works it is not considered. By taking all these drawbacks into account, a hybrid particle swarm optimization (PSO) method is proposed for the OPF problem with FACTS controller considering power loss and cost. The proposed hybrid PSO includes two stages of PSO and neural network. In the first stage of PSO, the amount of power generated by each generator for a particular load demand is estimated by satisfying different constraints with low cost and in the second stage, the voltage and angle to be injected for all the possible lines flows between buses except the slack bus are estimated. A neural network is used here for identifying the exact placement of FACTS controller. If the starting bus number is given as input, then the corresponding bus (ending bus) where the FACTS controller is to be connected, the voltage and angle injecting values are obtained as output. The result shows the performance of the proposed method with low cost and reduction in line losses.