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Soft Computing Techniques for Optimal
Capacitor Placement
Pradeep Kumar and Asheesh K. Singh
Abstract Distribution system transfers electric energy from the transmission sys-
tem 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 pro
le. Utilities utilize the
capabilities of the shunt capacitors to provide reactive power, for reducing the
power losses and improve the voltage pro
le. The extent of distribution losses
reduction and voltage pro
le improvement depends upon the location of these
capacitors in the system. Thus, optimal capacitor placement (OCP) becomes a
problem of signi
cance. 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 com-
puting 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.
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