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4.2.1 Analytical Method
Initially, when the computing resources were limited, the methods were based on
the analytical techniques. For capacitor placement, the analytical approaches used
(Levitin et al.
2000
; Fogel et al.
2000
) were based on the maximum economic
savings, as given by (
12
). These initial methods paved the path for the two-third
rule. It advocates installation of the capacitor of rating two-third of the peak reactive
load, at a position two-third of the distance along the total feeder length. However,
it is still used by many utilities, as being based on the assumptions like,
1. Constant feeder conductor sizes,
2. Uniform current loading, and
3. All variables are assumed continuous.
cations in the techniques using sectionalized normalized equivalent
feeders to overcome the assumptions (Lee and Grainger
1981
) led to more accurate
results.
Later, modi
4.2.2 Numerical Programming Methods
With advancement in computation facilities, numerical programming methods were
developed. These iterative procedures maximize or minimize the objective function,
satisfying some set of constraints, viz. voltage, capacitor sizes, etc. These
approaches allow the inclusion of the mixed variables, i.e., continuous voltage and
line loading, and discrete sizes of capacitors. The objective problem for the
capacitor takes the same form as (
12
). Several techniques such as dynamic pro-
gramming, mixed-integer programming, and integer programming (Aman et al.
2014
; Ng et al.
2000
) have been developed to solve these problems.
4.2.3 Soft Computing Methods
Soft computing methods utilize the idea and inspiration from the experiences,
natural evolution, and adaptation to solve the real word computational problems in
an ef
cient and robust manner (Fogel et al.
2000
). The problems, to be dealt with,
are non-linear in nature, which are either unsolvable or inaccurately solved using
conventional techniques. These soft computing methods provide fast and practical
strategies that utilize the exhaustive search space to provide a near optimal solution.
Generally, in soft computing techniques, the terminologies utilized are based on
the terminology of the inspiration to re
ect their connections, such as in genetic
algorithms, we have genotypes, phenotypes, species, etc. (Fogel et al.
2000
).
fl
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