The document discusses using genetic algorithm (GA) and particle swarm optimization (PSO) techniques to optimize unified power flow controller (UPFC) parameters in electric power transmission systems. It describes how UPFC works and the control objectives of minimizing losses and improving voltage stability. Both GA and PSO are population-based optimization methods inspired by natural evolution/biology. The document compares their application in optimizing UPFC placement and settings, finding that PSO provides faster results while GA may achieve better optimal solutions given more iterations. Test results on sample transmission systems demonstrate that both techniques successfully enhance system performance when UPFC is incorporated.

1. By- RANJANA
Co- author : Sandeep Kakran
NIT Kurukshetra

2.  Electrical power energy demand growth has led to an
amplified stress and increased danger of faults on the
transmission line.
 Power flow computed in the transmission lines is controlled
by the network characteristics such as:
 stability limit
 thermal limit
 voltage limit
 These limitations are removed by accumulation of new
transmission and generation capacity i.e. using Flexible
Alternating CurrentTransmission Systems (FACTS) devices

3.  There are many FACTS devices (power-
electronics-based) used to resolve the problems
associated with the efficient control of power
through the load flow distribution to improve
transmission capability, reactive compensated
power, providing the series and shunt
compensation and increase power quality.
 In conventional ideas of energy transmission,
the Unified Power Flow Controller (UPFC) is
utilized to control power quickly or specifically

4.  The UPFC essentially
includes of two voltage
source converters that
are associated
consecutively, the first is
arrangement converter
that is associated by an
arrangement
transformer and second
is shunt converter is
associated by a parallel
transformer.

5.  Genetic algorithm (GA) is
mathematically stimulated by the
process of natural selection that fits
to the superior class of evolutionary
algorithm.
 Genetic algorithm is used to
generate high-quality solution
to optimization and search problem
depending on bio-inspired operators
such as mutation, crossover and
selection.
 This method uses principle of
ordinary evolution and the
population of genetic to explore the
results to reach at high class solution
that is near to global solution.

7.  PSO (particle swarm optimization)
technique is used to find the best of feasible
solution to an optimization problem.
 In PSO algorithm, all particles are started
randomly and evaluated to calculate the
fitness for giving conclusion of the personal
best (best value of each particle) and global
best (best value of particle in the swarm).
Then a loop starts to create an optimum
solution.

8.  In the started loop, first the particle velocity is
reorganized by the personal best and global
bests, and then position of every particle is
restructured by the present velocity.
 This exclusive feature of PSO overcomes the
rapid convergence difficulty and improves the
search capability.
 Also it is different from the other methods,
because the result value doesn’t depend on the
initial population.

9.  Every particle in
PSO technique in
the D-dimensional
problem of space
with a velocity
energetically
allows applying the
rapid change of its
personal particle
and other particles

10.  The best prior position (that gives the value
of best) of the particle is calculated and
showed as that is called Pbest.
 Index of the particle which is best between
particles in the present population is denoted
by the letter g.The location of P and g is
symbolized as particle moves on extreme
velocity.

11.  Power data limits that the series converter is rated 100
MVA with maximum voltage injection 1.0pu.
 The shunt converter of UPFC is also rated 100MVA.
 The control constraints of the shunt converter are
voltage regulation mode and the series converter
which deliver power flow in a controlled manner.
 PSO and GA technique are used to find the best of
possible solution of optimization problem. Consider
the global optimal of an n-dimensional function when
a loop start toward finding an optimum solution

12. SIMULINK model of the UPFC controlling power of 500/230 KV Power System

13.  The UPFC consists of the two (VSI) voltage
source converter that are connected in back-to-
back manner, considered as the Shunt Converter
and the Series Converter as appeared in Figure
 are decided from the basic DC interface voltage
kept up by the DC storing capacitor.
 In normal operation of the stage edge of
arrangement voltage converter is chosen
autonomously of line current that lies in the
vicinity of 0 and 2π and its extent is changing in
range in the vicinity of zero and most extreme.

15.  The UPFC can be given by two controllable voltage sources:
 generated by the shunt connectedVSI, behind the leakage reactance
x
 generated by the series connectedVSI, behind the leakage reactance
x
 Objective function is defined below with control parameters
 Vo andVy are phasor and vDC is voltage across the DC link
 Modifying the values of m1, m2, ϕ1, ϕ2 can control or regulate the
voltages by the series and shunt converters of UPFC

16.  The proposed algorithm methods are used for UPFC
placement for the objectives of series and shunt
compensation considered by PSO and GA, is placed on
the line to reduce of maximum loss and poor voltage
profile.
 When UPFC is not connected, the system output at
various time periods have variation in real power (P),
reactive power (Q) and voltage (V) during load increase
 UPFC comprises of 2 converters working in consecutive
way associated from DC connection given by dc
stockpiling capacitor.
 Both converters freely create or assimilate responsive
power at their air conditioning yield terminal.

17. FOR SERIES:
Results for GA Results for PSO

18. Results for GA Results for PSO
FOR SHUNT

19.  PSO technique is superior to that of GA strategy in the
beginning of streamlining, yet when there is expansion in the
number of iterations the execution of GA is superior to that
of PSO.
 minimum installation cost for both methods is different. For
PSO method, the cost is more than the cost attained by GA.
The optimum solution of the parameters setting is reached
by the both methods. PSO technique faster than that of GA
because of in GA having steps selection, crossover, and
mutation.

20.  Particle Swarm Optimization (PSO) is practically search method
that based on the knowledge of the behavior of swarming in
biological populations. PSO is similar to Genetic Algorithm (GA)
because both methods are population based approaches and they
both are depends on the information of sharing between the
population members that increase their searching processes by
using combination actual and probabilistic rule.
 GA has fixed algorithm with various types and the uses. GA is very
helpful to when the inventor have not detailed domain because
GA possesses the ability to discover and rivet from their domain.
 PSO is applied to multi-objective problems in which fitness
evaluation take power into the account when moving the PSO
particle. The objective of this paper is to test hypothesis that
define the PSO and GA average yield the same effectiveness of
solution, PSO is the more efficient ( because of uses less number
of function evaluations) than the GA.

21. PSO and GA methods have been used to find the decrement in the
power loss and increment in voltage profile in the transmission
system.The following conclusions have identified:
 The outcomes demonstrate that power misfortune is lessened and the
voltage profile is kept up within indicated restrictions under various
load conditions.
 The results estimated from usage of the two strategies showed that
on both techniques the systems gives well values.This implies that the
system stability is improved with the proposed methods and reducing
the losses to a considerate level and high level efficiency can be
achieved.
 PSO gives quicker and better response than GA during comparison.
The real power losses and the voltage stability are found better with
the PSO algorithm than the GA.

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