Soft Computing Techniques for Optimal Capacitor Placement

Abstract

Distribution system transfers electric energy from the transmission system to electric loads. Majority of losses in power system, i.e., nearly 10 %, occur in distribution system. Rigid distribution system infrastructure and rising load demand lead to increase in losses, thus, degrading the voltage profile. Utilities utilize the capabilities of the shunt capacitors to provide reactive power, for reducing the power losses and improve the voltage profile. The extent of distribution losses reduction and voltage profile improvement depends upon the location of these capacitors in the system. Thus, optimal capacitor placement (OCP) becomes a problem of significance. The problem of OCP is bifurcated into two sub-problems, (i) selection of candidate buses for capacitor placement, and (ii) sizing of the capacitors at the candidate buses. To select candidate buses for OCP, analytical techniques are used. But, soft computing techniques are utilized for sizing the capacitors. As the problem being, both, continuous and discrete in nature, i.e., mixed-integer type, its solution using classical optimization methods becomes impractical, as they are prone to be trapped in local minima. Therefore, soft computing techniques, like genetic algorithms (GA), particle swarm optimization (PSO), Nelder-Mead particle swarm optimization (NM-PSO), etc., capable of providing the global optimum solution, are utilized to obtain a better solution to the OCP problem. Further, a discussion of the previously used analytical techniques and the numerical techniques along with their disadvantages over the soft computing techniques is presented. This chapter is intended to discuss the application issues related to the solution of OCP using soft computing techniques. Further, special emphasis is given to the modeling of the distribution system and capacitor placement problem (CPP), with the relevance of OCP in distributed generation.