SlideShare a Scribd company logo
A SEMINAR
ON
Reduction of Losses in Radial Distribution
System using Genetic Algorithm
By:-
ABHISHEK JANGID
B-Tech. EE-final year
Roll No.:12EAXEE702
1
 Introduction
 Problem Formulation
 GA and LSF Technique
 Solution algorithm for capacitor placement
 Result Analysis
 Conclusion
 References
2
 The increase in power demand and high load density in the
urban areas makes the operation of power systems complicated
and increases the line losses.
 To reduce these system losses, many papers have been
published and many research works have done in recent years
referring to optimal distribution planning.
 Various methods have been used to reduce power losses
economically. Optimal selection of capacitors, optimal selection
of conductors, and feeder reconfiguration are among different
ways of decreasing losses.
 One of the most important methods to reduce losses in the
radial distribution systems is the utilization of the shunt
capacitors.
3
 Power factor correction
 Feeder-Loss Reduction
 Release of System capacity
 Voltage- Stabilization/Regulation
 Efficient Power Utilization
 Power Quality Enhancement
4
1. The capacitor placement in distribution network is an
optimization problem. Various approaches are identified by
researchers. All approaches differ from each other by way of
their problem formulation and problem solution methods
employed.
2. The objective of this work is to reduce the energy losses in the
system and maintain the voltage magnitudes of the system with
in prescribed limit. Power flow evaluation in the system
Includes the calculation of bus voltages and line flows of a
network.
5
6
The power loss in each branch is given by:
total power loss of the system is given by:
• Genetic Algorithm (GA) is a global search and optimization
technique which is based on the mechanism of natural selection
and genetics. The development of GA is mostly attributed to the
work of Goldberg and Holland.
• GA is initiated with random criterion of initial population which
represents possible solution of the optimization problem. The
fitness of each individual is evaluated by the value of the
objective function which is called as fitness function. The new
population is formed by selecting the more fit individuals using
Genetic operators(selection, crossover and mutation) until the
assigned maximum number of generations are reached or some
form of convergence criterion has been met. Finally the
population stabilizes and most of the individuals in the
population are found to be almost identical.
7
 [Start] Generate random population of n chromosomes (suitable
solutions for the problem)
 [Fitness] Evaluate the fitness f(x) of each chromosome x in the
population.
 If function is satisfied after step 2 then stop and return to the
best solution otherwise go to the next step.
 [New population] Create a new population by repeating
following steps until the new population is complete
• [Selection] Select two parent chromosomes from a population
according to their fitness (the better fitness, the bigger chance to
be selected)
8
◦ [Crossover] With a crossover probability cross over the
parents to form a new offspring (children). If no crossover
was performed, offspring is an exact copy of parents.
◦ [Mutation] With a mutation probability mutate new offspring
at each locus (position in chromosome).
◦ [Accepting] Place new offspring in a new population
 [Replace] Use new generated population for a further run of
algorithm
 [Test] If the end condition is satisfied, stop, and return the best
solution in current population
 [Loop] Go to step 2
9
Steps used for the placement of shunt capacitors through Genetic
algorithm
 Step1- Read system data (Bus data and line data).
 Step2- Calculate Y bus and perform load flow analysis to find
out the voltage magnitude and power flow in branches.
 Step3- Perform optimization process by GA and find optimal
location and size of capacitors that
has to be placed.
 Step4- Place the capacitor at appropriate location as directed by
GA.
10
11
START
Input
parameters
GEN=1
Randomly generate initial solution
Find the score of each individual in the current population
Check for
convergence
Is Gen=Max.
Generation
STOP
STOP
Select parents based on their score
Produce children by application of Genetic Operators
GEN=GEN+1
Replace the current population with children to form next Generation
In order to determine the bus location for placing the capacitor at
that particular node in the radial distribution system, sensitivity
analysis method is employed. The evaluation of these locations
helps in reducing the search space during optimization process as
it has to optimize the size of capacitor not location. The
sensitivity analysis is a method to select location that reduces the
system real power losses when we place the capacitor at those
locations.
The loss sensitivity factor is calculated (LSF) at all the buses
using the equation given as
After the calculation of LSF at all the buses, all the values of
arrange in descending order so as to find out the most sensitive
node where capacitor has to be placed. 12
Steps used for the placement of shunt capacitors through LSF –GA
 Step1- Read system data (Bus data and line data).
 Step2- Calculate Y bus and perform load flow analysis to find out the
voltage magnitude and power flow in branches.
 Step3- Determine Node location through LSF and then perform GA
to find optimal size of capacitor that has to be placed on that
particular node.
 Step4- Place the capacitor at appropriate location which determine in
previous.
13
Location and sizing of capacitor determined through GA
Location and sizing of capacitor determined through combined
approach of LSF-GA
14
7 10 14 135 37.3 12.8 10.745.534.2
4 9 13 5 2 41.4 29.1 12.5 47.4 29.2
Location and sizing of capacitor determined through GA
Location and sizing of capacitor determined through combined
approach of LSF-GA
15
4 22 2 324 43.1 14.7 31.615.141.3
25 17 4 3 2 26.8 25.8 44.7 39.3 42.1
Location and sizing of capacitor determined through GA
Location and sizing of capacitor determined through combined
approach of LSF-GA
16
10 13 30 1129 48.2 46.4 40.139.149.3
15 14 31 10 13 47.4 47.4 48.4 49.2 47.8
17
No. of
Capacitor
Location Size (kVAr) Losses (kW) Voltage before
capacitor
Voltage after
capacitor
Elapsed time
(CPU time) in sec
1 9 30.546 13.374 1.049 1.066 148.791385
2 5 25.349 13.315 1.033 1.038 170.202313
9 22.978 1.049 1.061
3 5 24.625 13.274 1.033 1.036 210.280912
9 23.582 1.049 1.061
13 10.192 1.037 1.060
4 9 27.601 13.268 1.049 1.062 214.970340
13 11.644 1.037 1.061
3 19.707 1.030 1.034
5 22.231 1.033 1.035
5 5 20.843 13.259 1.033 1.035 239.528647
10 11.185 1.059 1.065
9 16.006 1.049 1.060
3 20.480 1.030 1.036
13 10.717 1.037 1.062
1. Effect of capacitor placement on the system losses is observed
by incrementing the number of capacitors in the system.
2. It is clearly observed that when a single capacitor is placed on
bus 9, losses of the system are 13.374 kW however a small
reduction in losses is observed when we increase the number of
capacitor to 5.
18
No. of
Capacitor
Capacitor
Location
Size (kVAr) Losses (kW)
Capacitor
Location
Size (kVAr)
Losses
(kW)
1 9 24.916 10.533 9 28.065 10.535
2 9 20.476 10.476 9 39.033 10.503
5 19.462 6 17.673
3 9 24.565 10.452 9 35.271 10.476
13 12.165 6 10.792
5 14.227 13 17.377
4 13 10.021 10.418 9 41.600 10.428
3 15.623 6 13.291
9 19.722 13 11.517
5 21.064 3 18.693
5 10 14.753 10.341 9 37.313 10.374
5 18.630 6 22.345
9 12.362 13 22.819
3 15.420 3 17.575
13 10.461 7 16.902
1. Light loading conditions, when the number of capacitors is
two, then the location provided by GA is on bus no. 9 and 5 it
is bus 9 and bus 6 from LSF calculation and losses under this
operating condition are 10.476 and 10.503 from GA and LSF
approach.
2. With higher number of capacitors, results obtained through GA
are more realistic.
19
No. of
Capacitor
Capacitor
Location
Size (kVAr) Losses (kW)
Capacitor
Location
Size (kVAr) Losses (kW)
1 9 34.064 16.231 9 35.583 16.238
2 9 25.447 16.145 9 38.692 16.190
5 22.911 14 15.727
3 13 10.994 16.100 9 45.483 16.114
5 33.737 14 15.385
9 24.359 6 26.424
4 6 13.497 16.056 9 42.078 16.078
13 13.398 14 14.509
10 24.979 6 40.984
9 39.687 13 15.167
5 6 10.095 15.982 9 46.808 15.986
9 44.625 14 15.007
5 28.838 6 48.714
13 11.585 13 14.696
2 33.659 2 49.538
1. It is clearly observed that at base case with no capacitor in the
system the losses are 16.329 kW.
2. After placement of five capacitors it reduces to 15.982 kW for
first approach and it is 15.986 kW by LSF method. This
suggests that location identification through GA is a better
choice.
No. of
Capacitor
Capacitor
Location
Size (kVAr) Losses (kW)
Capacitor
Location
Size (kVAr) Losses (kW)
1 5 48.506 23.990 4 49.781 23.992
2 5 35.156 23.661 4 45.024 23.791
9 35.898 9 42.684
3 2 37.641 23.569 4 44.152 23.717
5 40.609 9 35.520
9 34.040 13 41.805
4 2 39.546 23.500 4 43.790 23.531
5 41.199 9 37.217
9 31.320 13 12.907
13 10.341 5 43.572
5 7 37.325 23.389 4 41.448 23.428
10 12.896 9 29.185
14 34.292 13 12.549
5 45.589 5 47.403
13 10.709 2 29.278
20
1. It is clearly observed from table that at base case with no
capacitor in the system the losses are 24.00 kW. After
placement of five capacitors it reduces to 23.389kW for first
approach and it is 23.428 kW by LSF method.
2. The locations for various capacitors at bus 7, 10, 14,5,13 by
GA and 4, 9, 13, 5 and 2 by using LSF. This suggests that
location identification through GA is a better choice.
21
1. Above table shows the calculation of Loss Sensitivity factor
for IEEE 14 bus system under different loading conditions.
2. The amount of LSF is the indication of the suitable
candidate for the placement of shunt capacitors.
14 Bus
Loading
Condition
10% Redu. 10% Inc. 30% Inc.
Loss
Sensitivity
Factor
weak Bus
Loss Sensitivity
Factor
weak Bus Loss Sensitivity Factor weak Bus
1.00 9 1.00 9 0.66 4
0.50 6 0.89 14 0.40 9
0.45 13 0.65 6 0.37 13
0.40 3 0.50 13 0.30 5
0.33 7 0.41 2 0.25 2
0.29 5 0.33 7 0.22 7
0
8
16
24
32
1 2
3
4
5
Losses
No.of Capacitor
Losses (kW)
Base Case
Light Load
Medium Load (10%)
Medium Load (20%)
Heavy Load (30%)
Heavy Load (40%)
22
0
10
20
30
1 2
3
4
5
losses
No. of Capacitor
Losses (kW)
Base Case
Light Load
Medium Load (10%)
Medium Load (20%)
Heavy Load (30%)
Heavy Load (40%)
23
0 50 100 150 200
13.25
13.3
13.35
13.4
13.45
Optimal Capacitor Placement By Genetic Algorithm
Number of Generation
LossesMinimum(kw)
24
25
1. It is clearly observed from the table that when a single capacitor
is placed on bus 4, losses of the system were 17.639 kW however
a small reduction in losses is observed when we increase the
number of capacitor to 5.
No. of
Capacitor
Capacitor
Location
Size (kVAr) Losses (kW)
Voltage
before
capacitor
Voltage after
capacitor
Elapsed time (CPU
time) in sec
1 4 41.909 17.639 1.003 1.030 223.273913
2 4 37.689 17.479 1.003 1.026 240.145030
24 13.335 0.991 1.030
3 4 36.127 17.315 1.003 1.024 254.594486
24 13.425 0.991 1.046
10 33.050 1.014 1.058
4 3 21.665 17.214 1.014 1.038 260.006960
10 38.038 1.014 1.062
24 11.319 0.991 1.048
8 35.953 0.993 1.024
5 24 13.097 17.026 0.991 1.038 294.896335
26 40.204 0.977 1.059
12 22.699 1.046 1.063
10 42.959 1.014 1.049
3 15.308 1.014 1.038
26
1. Light loading conditions, when a single shunt capacitor is
placed on bus 21, loss is reduced from 14.02 to 13.869
whereas using LSF is found to be maximum for bus 20 and
the loss is reduced to 13.961.
2. When the number of capacitors is two, the losses under this
operating condition are 13.790 and 13.867 from GA and LSF
approach.
No. of
Capacitor
Capacitor
Location
Size (kVAr) Losses (kW)
Capacitor
Location
Size (kVAr) Losses (kW)
1 21 14.539 13.869 20 18.154 13.961
2 4 28.674 13.790 20 24.533 13.867
21 12.819 24 18.968
3 24 16.425 13.751 20 20.169 13.787
4 24.830 24 17.274
8 21.276 3 26.675
4 8 22.331 13.722 20 24.606 13.746
21 14.338 24 18.209
4 28.043 3 28.571
19 10.185 21 17.891
5 4 22.922 13.032 20 17.348 13.523
7 13.196 24 15.455
23 10.395 3 28.377
8 21.690 21 17.100
21 14.047 7 16.423
27
1. It is observed that with no capacitor in the system the losses
are 22.697kW.After placement of five capacitors it reduces to
21.345 kW for first approach and 21.425 kW by LSF method.
2. The location of shunt capacitors are 21, 7, 8, 4 and 24
determined through GA while 3, 4, 24, 21, and 27 through
LSF approach.
No. of
Capacitor
Capacitor
Location
Size (kVAr) Losses (kW)
Capacitor
Location
Size (kVAr) Losses (kW)
1 4 42.836 21.961 3 44.823 22.100
2 22 20.626 21.776 3 36.833 21.872
4 41.949 4 46.134
3 3 19.850 21.660 3 34.811 21.675
24 14.208 4 46.487
4 42.150 24 16.322
4 24 10.838 21.556 3 37.764 21.563
9 30.173 4 46.292
21 13.076 24 11.455
4 45.893 21 25.102
5 21 18.263 21.345 3 34.938 21.425
7 13.200 4 44.168
8 26.290 24 17.039
4 37.437 21 24.780
24 14.789 27 12.002
28
1. It is observed that with no capacitor in the system the losses
are 33.98 kW. After placement of five capacitors it reduces to
31.333 kW for first approach and it is 31.415 kW by LSF
method.
2. As number of capacitors increased results obtained through
GA are more realistic as size as well as losses calculated by
the GA is less than LSF approach.
No. of
Capacitor
Capacitor
Location
Size (kVAr) Losses (kW)
Capacitor
Location
Size (kVAr) Losses (kW)
1 21 34.159 33.029 25 39.474 33.488
2 4 42.259 32.261 25 42.556 33.011
21 28.360 17 34.292
3 24 23.639 31.908 25 33.332 32.294
3 42.977 17 28.724
7 35.319 4 47.193
4 10 43.915 31.538 25 28.242 31.623
7 32.390 17 26.935
3 44.961 4 48.442
24 17.766 3 47.557
5 4 43.152 31.333 25 26.813 31.415
22 14.724 17 25.863
2 41.356 4 44.740
24 15.118 3 39.399
3 31.628 2 42.150
29
1. Above table shows the calculation of Loss Sensitivity factor
for IEEE 14 bus system under different loading conditions.
2. The amount of LSF is the indication of the suitable candidate
for the placement of shunt capacitors.
30 Bus
Loading
Condition 10% Redu. 10% Inc. 30% Inc.
Loss Sensitivity
Factor
weak Bus
Loss Sensitivity
Factor
weak Bus
Loss Sensitivity
Factor
weak Bus
4.30 20 4.00 3 12.0 25
3.00 24 3.70 4 10.8 17
2.60 3 2.80 24 6.00 4
2.20 21 2.00 21 3.00 3
1.50 7 1.60 27 2.46 2
1.30 10 1.50 26 1.59 29
0
8
16
24
32
40
1 2
3
4
5
Losses
No.of capacitor
Losses (kW)
Base Case
Light Load
Medium Load (10%)
Medium Load (20%)
Heavy Load (30%)
Heavy Load (40%)
30
0
8
16
24
32
40
48
1 2
3
4
5
losses
No. of Capacitor
Losses (kW)
Base Case
Light Load
Medium Load (10%)
Medium Load (20%)
Heavy Load (30%)
Heavy Load (40%)
31
0 50 100 150 200
17.00
17.026
17.6
17.7
17.8
17.92
Optimal Capacitor Placement By Genetic Algorithm
Number of Generation
LossesMinimum(kW)
32
33
No. of
Capacitor
Capacitor
Location
Size (kVAr) Losses (kW)
Voltage
before
capacitor
Voltage after
capacitor
Elapsed time
(CPU time) in
sec
1 12 49.027 173.249 0.958 0.987 219.035626
2 12 47.045 167.914 0.958 0.989 227.778733
14 42.895 0.934 0.970
3 10 38.422 163.304 0.961 0.991 239.630848
11 43.340 0.959 0.980
29 43.240 0.969 0.994
4 29 48.276 160.733 0.969 0.991 248.594394
11 47.118 0.959 0.977
22 47.288 0.964 0.992
13 49.650 0.952 0.975
5 21 46.158 156.805 0.965 0.989 260.236893
12 48.171 0.958 0.976
25 43.545 0.963 0.982
22 48.068 0.964 0.984
10 38.806 0.961 0.977
1. It is clearly observed that when a single capacitor is placed
on bus 12, losses of the system were 173.249 kW from
178.735.
2. A significant reduction in losses is observed when the
number of shunt capacitors is increased up to 5. Location of
shunt capacitor is determined by GA.
34
1. Under light loading conditions placement of single capacitor
on bus 13 (GA) and bus 15 (LSF) results in reduction of from
141.698 to 136.951 and 137.943.
2. When the number of capacitors is two, then the location is on
bus no. 12 and 29 (GA) however it is bus 15 and bus 10
(LSF) with losses under this condition are 133.420 and
134.853
No. of
Capacitor
Capacitor
Location
Size (kVAr) Losses (kW)
Capacitor
Location
Size (kVAr) Losses (kW)
1 13 48.867 136.951 15 49.532 137.943
2 12 48.187 133.420 15 48.688 134.853
29 44.590 10 46.116
3 29 38.138 129.460 15 45.936 131.124
12 46.195 10 49.806
13 49.038 14 49.959
4 25 45.269 121.304 15 45.366 128.162
14 46.866 10 49.531
22 49.215 14 48.951
29 45.018 30 46.980
5 13 46.164 120.416 15 49.453 125.980
14 42.324 10 48.717
10 44.166 14 46.238
29 45.949 30 48.784
25 48.024 13 48.957
35
1. It is observed that at base case with no capacitor in the system
the losses are 221.196 kW. After placement of five capacitors
it reduces to becomes 192.045 kW for first approach and it is
195.651 kW by LSF method.
2. With increase in number of capacitors a significant reduction
in losses are evaluated.
No. of
Capacitor
Capacitor
Location
Size (kVAr) Losses (kW)
Capacitor
Location
Size (kVAr) Losses (kW)
1 12 49.145 214.623 15 49.233 215.969
2 12 46.534 209.470 15 48.663 210.157
13 46.756 13 49.219
3 12 48.823 205.538 15 47.503 206.146
14 47.539 13 48.875
25 41.359 10 44.218
4 22 48.983 193.973 15 48.173 200.720
11 46.989 13 49.727
13 47.657 10 47.663
25 48.332 29 47.995
5 29 47.508 192.045 15 46.353 195.651
12 48.555 13 49.750
30 45.873 10 48.217
14 47.668 29 47.960
11 46.620 12 49.807
36
1. It is observed that at base case with no capacitor in the
system the losses are 324.383 kW. After placement of five
capacitors it becomes 284.038 kW (GA) and it is 289.386
kW (LSF).
2. The locations for various capacitors at bus 10, 13, 30, 29
and 11 by GA while 15, 14, 31, 10 and 13 through LSF.
No. of
Capacitor
Capacitor
Location
Size (kVAr) Losses (kW)
Capacitor
Location
Size (kVAr) Losses (kW)
1 12 48.307 315.607 15 49.845 317.143
2 13 49.387 308.030 15 49.400 310.042
11 44.357 14 48.542
3 9 47.540 300.098 15 49.493 307.247
13 48.822 14 49.735
12 45.008 31 47.482
4 11 48.579 294.642 15 47.723 298.390
25 44.020 14 47.409
13 47.357 31 49.939
10 48.423 10 49.599
5 10 48.215 284.038 15 47.475 289.386
13 46.481 14 47.460
30 49.337 31 48.486
29 39.158 10 49.219
11 40.112 13 47.847
37
1. Above table shows the calculation of Loss Sensitivity factor
for IEEE 14 bus system under different loading conditions.
2. The amount of LSF is the indication of the suitable candidate
for the placement of shunt capacitors.
33 Bus
Loading
Condition
10% Redu. 10% Inc. 30% Inc.
Loss Sensitivity
Factor
weak Bus
Loss Sensitivity
Factor
weak Bus
Loss Sensitivity
Factor
weak Bus
11.0 15 19.0 15 17.7 15
6.75 10 5.45 13 15.5 14
2.75 14 2.92 10 3.35 31
2.06 30 1.90 29 2.53 10
0.49 13 0.55 12 0.62 13
0.39 32 0.46 32 0.47 30
0
75
150
225
300
375
1 2
3
4
5
Losses
No. of Capacitor
Losses (kW)
Base Case
Light Load
Medium Load (10%)
Medium Load (20%)
Heavy Load (30%)
Heavy Load (40%)
38
0
75
150
225
300
375
450
1 2
3
4
5
losses
No. of Capacitor
Losses (kW)
Base Case
Light Load
Medium Load (10%)
Medium Load (20%)
Heavy Load (30%)
Heavy Load (40%)
39
0 50 100 150 200
154
156
158
160
162
164
Optimal Capacitor Placement By Genetic Algorithm
Number of Generation
LossesMinimum(kW)
40
Thus we conclude that with the placement of shunt capacitor in
radial distribution system results in
1. Results are calculated for both approaches and compared for
different loading conditions.
2. GA optimization technique is effective in deciding the
position where different size capacitors to be placed, for
different number of candidate buses.
3. GA Search optimization technique generate more superior
results than LSF with GA optimization in terms of power loss
reduction.
4. Optimal placement and sizing of capacitors give improved
voltage profile and higher power loss reduction.
41
[1] HadiSaadat, “Power System Analysis”, McGraw-HiII Series in Electrical and
Computer Engineering, 1999.
[2] Mesut E. Baranand, Felix F. Wu, “Optimal capacitor placement on Radial
distribution systems”, IEEE Transactions on Power Delivery, Vol. 4, No. 1, January
1989.
[3] S.Sundhararajan, A.Pahwa, “Optimal selection of capacitors for radial distribution
systems using a genetic algorithm”,Power Systems, IEEE Transactions on , Vol. 9, No.
3, pages 1499-1507, August 1994.
[4] Hsiao-Dong Chiang, Jin-Cheng Wang, Jianzhong Tong,G. Darling, “Optimal
capacitor placement, replacement and control in large-scale unbalanced distribution
systems: system solution algorithms and numerical studies”,Power Systems, IEEE
Transactions on , Vol. 10, No.1, pages 363-369, February 1995.
[5] Hong-TzerYang, Yam-Chang Huang, Ching-Lien Huang, “Solution to capacitor
placement problem in a radial distribution system using tabu search method”,Energy
Management and Power Delivery, International Conference, Vol. 1, pages 388-393,
November 1995.
[6] M.H.Haque, “Capacitor placement in radial distribution systems for loss reduction”,
IEEE General Transmission Distribution,Vol. 146, No. 5, September 1999.
42
 [7] D.Richardson, “Identification of Capacitor Position in a Radial System”, IEEE
Transactions on Power Delivery, Vol.14, No.4,pages 1368-1373, October 1999.
 [8] J. C. Carlisle, A. A. El-Keib, “ A Graph Search Algorithm for Optimal Placement of
Fixed and Switched Capacitors on Radial Distribution Systems”, IEEE Transactions on
power delivery, Vol. 15, No. 1, January 2000.
 [9] A.Augugliaro, L. Dusonchet, E.R. Sanseverino, “An Evolutionary Parallel Tabu
Search approach for distribution systems reinforcement planning”, Advanced
Engineering Informatics, Vol. 16, pages 205–215, July 2002.
 [10] Young-Jae Jeon, Jae-Chul Kim, “Application of simulated annealing and tabu
search for loss minimization in distribution systems”, Electrical Power and Energy
Systems, Vol. 26, January 2004.
 [11] M. A. S. Masoum, M. Ladjevardi, E. F. Fuchs, W. M. Grady, “Application of local
variations and maximum sensitivities selection for optimal placement of shunt capacitor
banks under non sinusoidal operating conditions”, Int. J. Electrical Power Energy
System, Vol. 26, No. 10, pages.761–769, 2004.
 [12] Ying-Tung Hsiao, Chia-Hong Chen, Cheng-ChihChien, “Optimal capacitor
placement in distribution system using a combination fuzzy–GA method”, Electrical
Power and Energy Systems, Vol. 26, pages 501-508, September 2004.
43
 [13] Luis Rojas, Rodolfo Garcia, Luis Roa, “Optimal Capacitor Location for Radial
Systems using Genetic Algorithms”,Transmission & Distribution Conference and
Exposition 2006. TDC '06. IEEE/PES , Vol. 1, No. 4, pages 15-18, August 2006.
 [14] D. Das, “Optimal capacitor placement in radial distribution system using fuzzy-GA
method”, Electrical Power and Energy Systems, Vol. 30, pages 361-367, August 2007.
 [15] V.V.K. Reddy, M. Sydulu, “Index and GA based Optimal Location and Sizing of
Distribution System Capacitors”,Power Engineering Society General Meeting 2007.
IEEE, Vol. 1, No. 4, pages 24-28, June 2007.
 [16] A. Lakshmi Devi, B. Subramanyam, “Optimal dg unit placement for loss reduction
in Radial distribution system-a case study”, ARPN Journal of Engineering and Applied
Sciences, Vol. 2, No. 6, December 2007.
 [17] R.A. Jabr,“Optimal placement of capacitors in a radial network using conic and
mixed integer linear programming”, Electric Power Systems Research, Vol. 78, June
2008.
 [18] M. Damodar Reddy, V.C. Veera Reddy, “Optimal capacitor placement using fuzzy
and Real Coded Genetic Algorithm for maximum saving”, Journal of Theoretical and
Applied Information Technology, 2008.
44
 [19] Ahmad Galal Sayed, Hosam K.M. Youseef, “Optimal sizing of fixed capacitor
banks placed on a distorted interconnected distribution networks by Genetic
Algorithms”,Computational Technologies in Electrical and Electronics Engineering
2008. IEEE Region 8 International Conference, July 2008.
 [20] MarvastiVahid, M. Manouchehr, N. Hossein, S.D. Jamaleddin, “Combination
of optimal conductor selection and capacitor placement in radial distribution
systems for maximum loss reduction”,Industrial Technology 2009. ICIT 2009.IEEE
International Conference, Vol. 1, No.5, pages 10-13, February 2009.
 [21] T. SamimiAsl, S. Jamali, “Optimal capacitor placement size and location of
shunt capacitor for reduction of losses on distribution feeders”,Clean Electrical
Power, International Conference on, pages 223-226, June 2009.
 [22] IB Mady,“Optimal sizing of capacitor banks and distributed generation in
distorted distribution networks by genetic algorithms”,Electricity Distribution - Part
1, 2009. CIRED 2009, 20th International Conference and Exhibition, Vol. 1, No. 4,
pages 8-11, June 2009.
 [23] J. B. V. Subrahmanyam, “Optimal Capacitor Placement in Unbalanced Radial
Distribution Network”, Journal of Theoretical and Applied Information Technology,
Vol. 6, pages 106-115, 2009.
45
 [24] S.M. Hakimi, M. Zarringhalami, S. M. MoghaddasTafreshi , “Optimal
capacitor placement and sizing in non-radial distribution to improve power
quality”,14th International Conference, Vol 1, No. 6, pages 26-29, September 2010.
 [25] Anil Swarnkar,Nikhil Gupta, K. R. Niazi, “Optimal placement of fixed and
switched shunt capacitors for large-scale distribution systems using genetic
algorithms”,Innovative Smart Grid Technologies Conference Europe (ISGT Europe),
2010 IEEE PES, Vol. 1, No. 8, pages 11-13 October 2010.
 [26]
HamidRezaSalehi,AliRezaVahabzadeh,HosseinAskarianabyaneh,ForoughMahmoodi
anfard, “Optimal capacitor placement for loss reduction”, Modern electric Power
System, 2010
 [27]Seyed Abbas Taher, Mohammad Hasani, Ali Karimian, “A novel method for
optimal capacitor placement and sizing in distribution systems with non linear loads
and DG using GA”, Common Nonlinear SciNumerSimulat, Vol. 16, pages 851-
862,February 2011.
 [28] Jiachuan Shi, Chunyi Wang, Peng An, “Loop-Based Coding Reactive Tabu
Search for Comprehensive Optimization in Distribution Networks”,Power and
Energy EngineeringConference (APPEEC) 2011 Asia-Pacific, Vol. 1, No. 4, pages
25-28, March 2011.
46
 [29] Macros A.N. Guimaraes, Carlos A. Castro,“An efficient method for distribution
systems reconfiguration and capacitor placement using a Chu-Beasley based genetic
algorithm”,PowerTech, 2011 IEEE Trondheim, Vol. 1, No. 7, pages 19-23, June 2011.
 [30] Y. Mohamed Shuaib, C.ChristoberAsirRajan, “Capacitor Sizing and Placement on
Radial Distribution System Using Queen Bee Assisted Genetic Algorithm”,Process
Automation, Control and Computing (PACC), 2011 International Conference, Vol. 1, No.
8, pages 20-22, July 2011.
 [31] S.Neelima, Dr. P.S.Subramanyam, “Optimal capacitor placement in distribution
system using Genetic Algorithm: A Dimension Reducing Approach”, Journal of
Theoretical and Applied Information Technology, Vol.30, No.1, August 2011.
 [32] Majid Davoodi, Mohsen Davoudi, IrajGanjkhany, Ali Aref, “Optimal Capacitor
placement in Distribution Networks Using Genetic Algorithm”, Vol. 5 ,July 2012.
 [33] Deepti Sharma, AmitaMahor, “Optimal Placement of Capacitor in Radial
Distribution System Using Real Coded Genetic Algorithm”, International Journal of
Electrical Engineering, August2013.
[34] Attia A. El-Fergany, “Involvement of cost savings and voltage stability indices in
optimal capacitor allocation in radial distribution networks using artificial bee colony
algorithm”, International Journal of Electrical Power & Energy Systems, Vol. 62, Pages
608-616, November 2014.
 [35] www.mathworks.com
47
48
Genetic Algo. for Radial Distribution System to reduce Losses

More Related Content

PDF
Optimal Capacitor Placement in Distribution System using Fuzzy Techniques
IDES Editor
 
PDF
Performance Improvement of the Radial Distribution System by using Switched C...
idescitation
 
PDF
Optimal Capacitor Placement in a Radial Distribution System using Shuffled Fr...
IDES Editor
 
PPT
OPTIMAL PLACEMENT AND SIZING OF CAPACITOR BANKS BASED ON VOLTAGE PROFILE AND ...
Prashanta Sarkar
 
PDF
El36841848
IJERA Editor
 
PDF
Optimal Siting And Sizing Of Distributed Generation For Radial Distribution S...
inventy
 
PPT
Distributed generation placement
Satish Kansal
 
PDF
Optimal Placement of Distributed Generation on Radial Distribution System for...
IJMER
 
Optimal Capacitor Placement in Distribution System using Fuzzy Techniques
IDES Editor
 
Performance Improvement of the Radial Distribution System by using Switched C...
idescitation
 
Optimal Capacitor Placement in a Radial Distribution System using Shuffled Fr...
IDES Editor
 
OPTIMAL PLACEMENT AND SIZING OF CAPACITOR BANKS BASED ON VOLTAGE PROFILE AND ...
Prashanta Sarkar
 
El36841848
IJERA Editor
 
Optimal Siting And Sizing Of Distributed Generation For Radial Distribution S...
inventy
 
Distributed generation placement
Satish Kansal
 
Optimal Placement of Distributed Generation on Radial Distribution System for...
IJMER
 

What's hot (18)

PPTX
What is Distributed Generation
Ajay Singh
 
PDF
Impacts of Photovoltaic Distributed Generation Location and Size on Distribut...
International Journal of Power Electronics and Drive Systems
 
PDF
Genetic Algorithm based Optimal Placement of Distributed Generation Reducing ...
IDES Editor
 
PDF
D010312127
IOSR Journals
 
PDF
Hybrid bypass technique to mitigate leakage current in the grid-tied inverter
IJECEIAES
 
PDF
A CONTROL APPROACH FOR GRID INTERFACING INVERTER IN 3 PHASE 4 WIRE DISTRIBUT...
IJMER
 
PDF
Various demand side management techniques and its role in smart grid–the stat...
IJECEIAES
 
PDF
Loss Reduction by Optimal Placement of Distributed Generation on a Radial feeder
IDES Editor
 
PDF
Optimal placement and sizing of ht shunt capacitors for transmission loss min...
IAEME Publication
 
PDF
[IJET-V1I4P9] Author :Su Hlaing Win
IJET - International Journal of Engineering and Techniques
 
PDF
Optimal placement of distributed power flow controller for loss reduction usi...
eSAT Journals
 
PDF
G046033742
IJERA Editor
 
PDF
Power Quality Improvement with Multilevel Inverter Based IPQC for Microgrid
IJMTST Journal
 
PDF
Hybrid Power Supply using Improved H6 based MITCB DC – DC Converter for House...
IRJET Journal
 
PDF
A Novel Approach for Allocation of Optimal Capacitor and Distributed Generati...
paperpublications3
 
PDF
Reliability Assesment of Debremarkos Distribution System Found In Ethiopia
International journal of scientific and technical research in engineering (IJSTRE)
 
PDF
International Journal of Engineering Research and Development
IJERD Editor
 
PDF
Comparison of PI and ANN Control Techniques for Nine Switches UPQC to Improve...
MABUSUBANI SHAIK
 
What is Distributed Generation
Ajay Singh
 
Impacts of Photovoltaic Distributed Generation Location and Size on Distribut...
International Journal of Power Electronics and Drive Systems
 
Genetic Algorithm based Optimal Placement of Distributed Generation Reducing ...
IDES Editor
 
D010312127
IOSR Journals
 
Hybrid bypass technique to mitigate leakage current in the grid-tied inverter
IJECEIAES
 
A CONTROL APPROACH FOR GRID INTERFACING INVERTER IN 3 PHASE 4 WIRE DISTRIBUT...
IJMER
 
Various demand side management techniques and its role in smart grid–the stat...
IJECEIAES
 
Loss Reduction by Optimal Placement of Distributed Generation on a Radial feeder
IDES Editor
 
Optimal placement and sizing of ht shunt capacitors for transmission loss min...
IAEME Publication
 
Optimal placement of distributed power flow controller for loss reduction usi...
eSAT Journals
 
G046033742
IJERA Editor
 
Power Quality Improvement with Multilevel Inverter Based IPQC for Microgrid
IJMTST Journal
 
Hybrid Power Supply using Improved H6 based MITCB DC – DC Converter for House...
IRJET Journal
 
A Novel Approach for Allocation of Optimal Capacitor and Distributed Generati...
paperpublications3
 
Reliability Assesment of Debremarkos Distribution System Found In Ethiopia
International journal of scientific and technical research in engineering (IJSTRE)
 
International Journal of Engineering Research and Development
IJERD Editor
 
Comparison of PI and ANN Control Techniques for Nine Switches UPQC to Improve...
MABUSUBANI SHAIK
 
Ad

Viewers also liked (20)

PPTX
Distribution systems
SURAJ PRASAD
 
PDF
POWER LOSS REDUCTION IN ELECTRICAL DISTRIBUTION SYSTEMS USING CAPACITOR PLACE...
International Journal of Technical Research & Application
 
PDF
Determination of the Weakest Branch in a Radial Distribution System using Rea...
IDES Editor
 
PDF
My research in 2013 in English
Kazuma Nagafune
 
PDF
Efficient Optimal Sizing And Allocation Of Capacitors In Radial Distribution ...
IDES Editor
 
PDF
Assignment of measurement
Jitendra Bhadoriya
 
PPTX
Electrical simulation of radial artery using comsol.....presentation
Jalal Uddin
 
PDF
An optimized hybrid system model solution for coastal area in bangladesh
eSAT Journals
 
PPTX
Delivering distribution intelligence with MV/LV supervision and control
Landis+Gyr
 
PPTX
DISTRIBUTION SYSTEM OPERATION AND CONTROL
yohannes feleke
 
PPTX
Gridstream Evolution
Landis+Gyr
 
PPTX
HYBRID POWER GENERATION SYSTEM FOR DOMESTIC PIRPOSEES
Bhushith Kumar
 
PPTX
Optimal placement and sizing of multi dg using pso
Jitendra Bhadoriya
 
PDF
ELECTRICAL DISTRIBUTION TECHNOLOGY
Ameen San
 
PDF
seminar report on optimal placement and optimal sizing of DG
khemraj298
 
PPTX
AT&C LOSSES In RAPDRP
Abhishek Kumar
 
PDF
Electricity theft detection and localisation in unknown radial low voltage ne...
eSAT Journals
 
PDF
OPTIMAL PLACEMENT OF DISTRIBUTED GENERATION
Jitendra Bhadoriya
 
PDF
La consulta genètica i el pediatre. 2017
Pediatriadeponent
 
PPT
Smart Grids:Enterprise GIS For Distribution Loss Reduction in Electric Utilit...
HIMADRI BANERJI
 
Distribution systems
SURAJ PRASAD
 
POWER LOSS REDUCTION IN ELECTRICAL DISTRIBUTION SYSTEMS USING CAPACITOR PLACE...
International Journal of Technical Research & Application
 
Determination of the Weakest Branch in a Radial Distribution System using Rea...
IDES Editor
 
My research in 2013 in English
Kazuma Nagafune
 
Efficient Optimal Sizing And Allocation Of Capacitors In Radial Distribution ...
IDES Editor
 
Assignment of measurement
Jitendra Bhadoriya
 
Electrical simulation of radial artery using comsol.....presentation
Jalal Uddin
 
An optimized hybrid system model solution for coastal area in bangladesh
eSAT Journals
 
Delivering distribution intelligence with MV/LV supervision and control
Landis+Gyr
 
DISTRIBUTION SYSTEM OPERATION AND CONTROL
yohannes feleke
 
Gridstream Evolution
Landis+Gyr
 
HYBRID POWER GENERATION SYSTEM FOR DOMESTIC PIRPOSEES
Bhushith Kumar
 
Optimal placement and sizing of multi dg using pso
Jitendra Bhadoriya
 
ELECTRICAL DISTRIBUTION TECHNOLOGY
Ameen San
 
seminar report on optimal placement and optimal sizing of DG
khemraj298
 
AT&C LOSSES In RAPDRP
Abhishek Kumar
 
Electricity theft detection and localisation in unknown radial low voltage ne...
eSAT Journals
 
OPTIMAL PLACEMENT OF DISTRIBUTED GENERATION
Jitendra Bhadoriya
 
La consulta genètica i el pediatre. 2017
Pediatriadeponent
 
Smart Grids:Enterprise GIS For Distribution Loss Reduction in Electric Utilit...
HIMADRI BANERJI
 
Ad

Similar to Genetic Algo. for Radial Distribution System to reduce Losses (20)

PDF
40220140503002
IAEME Publication
 
PDF
19 avadhanam kartikeya sarma 177-185
Alexander Decker
 
PDF
AN EFFICIENT COUPLED GENETIC ALGORITHM AND LOAD FLOW ALGORITHM FOR OPTIMAL PL...
ijiert bestjournal
 
DOC
Optimal Capacitor Placement for Improving Electrical Performance in distribut...
AmmarHakami
 
PDF
Optimal Capacitor Placement for IEEE 14 bus system using Genetic Algorithm
AM Publications
 
PDF
New microsoft office word document (3)2
Mohamed El Houssiny
 
PDF
02 15033 distribution power loss minimization via (edit)
nooriasukmaningtyas
 
PDF
Optimizing location and size of capacitors for power loss reduction in radial...
TELKOMNIKA JOURNAL
 
PDF
11.power loss reduction in radial distribution system by using plant growth s...
Alexander Decker
 
PDF
Power loss reduction in radial distribution system by using plant growth simu...
Alexander Decker
 
PDF
Artificial bee colony algorithm based approach for capacitor allocation in un
IAEME Publication
 
PDF
Optimal Allocation of Capacitor Bank in Radial Distribution System using Anal...
IJECEIAES
 
PDF
Capacitor Placement and Reconfiguration of Distribution System with hybrid Fu...
IOSR Journals
 
PDF
Jh3516001603
IJERA Editor
 
PDF
Reconfiguration and Capacitor Placement in Najaf Distribution Networks Sector...
IRJET Journal
 
PDF
E021052327
Manoj Aryabhumi
 
PDF
A010430108
IOSR Journals
 
PDF
Optimal Placement and Sizing of Capacitor and Distributed Generator in Radial...
IJMTST Journal
 
PDF
Al36228233
IJERA Editor
 
PDF
Review on Optimal Allocation of Capacitor in Radial Distribution System
IRJET Journal
 
40220140503002
IAEME Publication
 
19 avadhanam kartikeya sarma 177-185
Alexander Decker
 
AN EFFICIENT COUPLED GENETIC ALGORITHM AND LOAD FLOW ALGORITHM FOR OPTIMAL PL...
ijiert bestjournal
 
Optimal Capacitor Placement for Improving Electrical Performance in distribut...
AmmarHakami
 
Optimal Capacitor Placement for IEEE 14 bus system using Genetic Algorithm
AM Publications
 
New microsoft office word document (3)2
Mohamed El Houssiny
 
02 15033 distribution power loss minimization via (edit)
nooriasukmaningtyas
 
Optimizing location and size of capacitors for power loss reduction in radial...
TELKOMNIKA JOURNAL
 
11.power loss reduction in radial distribution system by using plant growth s...
Alexander Decker
 
Power loss reduction in radial distribution system by using plant growth simu...
Alexander Decker
 
Artificial bee colony algorithm based approach for capacitor allocation in un
IAEME Publication
 
Optimal Allocation of Capacitor Bank in Radial Distribution System using Anal...
IJECEIAES
 
Capacitor Placement and Reconfiguration of Distribution System with hybrid Fu...
IOSR Journals
 
Jh3516001603
IJERA Editor
 
Reconfiguration and Capacitor Placement in Najaf Distribution Networks Sector...
IRJET Journal
 
E021052327
Manoj Aryabhumi
 
A010430108
IOSR Journals
 
Optimal Placement and Sizing of Capacitor and Distributed Generator in Radial...
IJMTST Journal
 
Al36228233
IJERA Editor
 
Review on Optimal Allocation of Capacitor in Radial Distribution System
IRJET Journal
 

Recently uploaded (20)

PDF
LEAP-1B presedntation xxxxxxxxxxxxxxxxxxxxxxxxxxxxx
hatem173148
 
PPTX
Inventory management chapter in automation and robotics.
atisht0104
 
PDF
67243-Cooling and Heating & Calculation.pdf
DHAKA POLYTECHNIC
 
PDF
Biodegradable Plastics: Innovations and Market Potential (www.kiu.ac.ug)
publication11
 
PPTX
Online Cab Booking and Management System.pptx
diptipaneri80
 
PPTX
Chapter_Seven_Construction_Reliability_Elective_III_Msc CM
SubashKumarBhattarai
 
PDF
Construction of a Thermal Vacuum Chamber for Environment Test of Triple CubeS...
2208441
 
DOCX
SAR - EEEfdfdsdasdsdasdasdasdasdasdasdasda.docx
Kanimozhi676285
 
PPTX
22PCOAM21 Session 2 Understanding Data Source.pptx
Guru Nanak Technical Institutions
 
PPTX
MT Chapter 1.pptx- Magnetic particle testing
ABCAnyBodyCanRelax
 
PDF
Cryptography and Information :Security Fundamentals
Dr. Madhuri Jawale
 
PPTX
database slide on modern techniques for optimizing database queries.pptx
aky52024
 
PDF
Introduction to Ship Engine Room Systems.pdf
Mahmoud Moghtaderi
 
PPTX
Module2 Data Base Design- ER and NF.pptx
gomathisankariv2
 
PDF
2025 Laurence Sigler - Advancing Decision Support. Content Management Ecommer...
Francisco Javier Mora Serrano
 
PPT
Understanding the Key Components and Parts of a Drone System.ppt
Siva Reddy
 
PPTX
MULTI LEVEL DATA TRACKING USING COOJA.pptx
dollysharma12ab
 
PDF
AI-Driven IoT-Enabled UAV Inspection Framework for Predictive Maintenance and...
ijcncjournal019
 
PDF
All chapters of Strength of materials.ppt
girmabiniyam1234
 
PDF
Machine Learning All topics Covers In This Single Slides
AmritTiwari19
 
LEAP-1B presedntation xxxxxxxxxxxxxxxxxxxxxxxxxxxxx
hatem173148
 
Inventory management chapter in automation and robotics.
atisht0104
 
67243-Cooling and Heating & Calculation.pdf
DHAKA POLYTECHNIC
 
Biodegradable Plastics: Innovations and Market Potential (www.kiu.ac.ug)
publication11
 
Online Cab Booking and Management System.pptx
diptipaneri80
 
Chapter_Seven_Construction_Reliability_Elective_III_Msc CM
SubashKumarBhattarai
 
Construction of a Thermal Vacuum Chamber for Environment Test of Triple CubeS...
2208441
 
SAR - EEEfdfdsdasdsdasdasdasdasdasdasdasda.docx
Kanimozhi676285
 
22PCOAM21 Session 2 Understanding Data Source.pptx
Guru Nanak Technical Institutions
 
MT Chapter 1.pptx- Magnetic particle testing
ABCAnyBodyCanRelax
 
Cryptography and Information :Security Fundamentals
Dr. Madhuri Jawale
 
database slide on modern techniques for optimizing database queries.pptx
aky52024
 
Introduction to Ship Engine Room Systems.pdf
Mahmoud Moghtaderi
 
Module2 Data Base Design- ER and NF.pptx
gomathisankariv2
 
2025 Laurence Sigler - Advancing Decision Support. Content Management Ecommer...
Francisco Javier Mora Serrano
 
Understanding the Key Components and Parts of a Drone System.ppt
Siva Reddy
 
MULTI LEVEL DATA TRACKING USING COOJA.pptx
dollysharma12ab
 
AI-Driven IoT-Enabled UAV Inspection Framework for Predictive Maintenance and...
ijcncjournal019
 
All chapters of Strength of materials.ppt
girmabiniyam1234
 
Machine Learning All topics Covers In This Single Slides
AmritTiwari19
 

Genetic Algo. for Radial Distribution System to reduce Losses

  • 1. A SEMINAR ON Reduction of Losses in Radial Distribution System using Genetic Algorithm By:- ABHISHEK JANGID B-Tech. EE-final year Roll No.:12EAXEE702 1
  • 2.  Introduction  Problem Formulation  GA and LSF Technique  Solution algorithm for capacitor placement  Result Analysis  Conclusion  References 2
  • 3.  The increase in power demand and high load density in the urban areas makes the operation of power systems complicated and increases the line losses.  To reduce these system losses, many papers have been published and many research works have done in recent years referring to optimal distribution planning.  Various methods have been used to reduce power losses economically. Optimal selection of capacitors, optimal selection of conductors, and feeder reconfiguration are among different ways of decreasing losses.  One of the most important methods to reduce losses in the radial distribution systems is the utilization of the shunt capacitors. 3
  • 4.  Power factor correction  Feeder-Loss Reduction  Release of System capacity  Voltage- Stabilization/Regulation  Efficient Power Utilization  Power Quality Enhancement 4
  • 5. 1. The capacitor placement in distribution network is an optimization problem. Various approaches are identified by researchers. All approaches differ from each other by way of their problem formulation and problem solution methods employed. 2. The objective of this work is to reduce the energy losses in the system and maintain the voltage magnitudes of the system with in prescribed limit. Power flow evaluation in the system Includes the calculation of bus voltages and line flows of a network. 5
  • 6. 6 The power loss in each branch is given by: total power loss of the system is given by:
  • 7. • Genetic Algorithm (GA) is a global search and optimization technique which is based on the mechanism of natural selection and genetics. The development of GA is mostly attributed to the work of Goldberg and Holland. • GA is initiated with random criterion of initial population which represents possible solution of the optimization problem. The fitness of each individual is evaluated by the value of the objective function which is called as fitness function. The new population is formed by selecting the more fit individuals using Genetic operators(selection, crossover and mutation) until the assigned maximum number of generations are reached or some form of convergence criterion has been met. Finally the population stabilizes and most of the individuals in the population are found to be almost identical. 7
  • 8.  [Start] Generate random population of n chromosomes (suitable solutions for the problem)  [Fitness] Evaluate the fitness f(x) of each chromosome x in the population.  If function is satisfied after step 2 then stop and return to the best solution otherwise go to the next step.  [New population] Create a new population by repeating following steps until the new population is complete • [Selection] Select two parent chromosomes from a population according to their fitness (the better fitness, the bigger chance to be selected) 8
  • 9. ◦ [Crossover] With a crossover probability cross over the parents to form a new offspring (children). If no crossover was performed, offspring is an exact copy of parents. ◦ [Mutation] With a mutation probability mutate new offspring at each locus (position in chromosome). ◦ [Accepting] Place new offspring in a new population  [Replace] Use new generated population for a further run of algorithm  [Test] If the end condition is satisfied, stop, and return the best solution in current population  [Loop] Go to step 2 9
  • 10. Steps used for the placement of shunt capacitors through Genetic algorithm  Step1- Read system data (Bus data and line data).  Step2- Calculate Y bus and perform load flow analysis to find out the voltage magnitude and power flow in branches.  Step3- Perform optimization process by GA and find optimal location and size of capacitors that has to be placed.  Step4- Place the capacitor at appropriate location as directed by GA. 10
  • 11. 11 START Input parameters GEN=1 Randomly generate initial solution Find the score of each individual in the current population Check for convergence Is Gen=Max. Generation STOP STOP Select parents based on their score Produce children by application of Genetic Operators GEN=GEN+1 Replace the current population with children to form next Generation
  • 12. In order to determine the bus location for placing the capacitor at that particular node in the radial distribution system, sensitivity analysis method is employed. The evaluation of these locations helps in reducing the search space during optimization process as it has to optimize the size of capacitor not location. The sensitivity analysis is a method to select location that reduces the system real power losses when we place the capacitor at those locations. The loss sensitivity factor is calculated (LSF) at all the buses using the equation given as After the calculation of LSF at all the buses, all the values of arrange in descending order so as to find out the most sensitive node where capacitor has to be placed. 12
  • 13. Steps used for the placement of shunt capacitors through LSF –GA  Step1- Read system data (Bus data and line data).  Step2- Calculate Y bus and perform load flow analysis to find out the voltage magnitude and power flow in branches.  Step3- Determine Node location through LSF and then perform GA to find optimal size of capacitor that has to be placed on that particular node.  Step4- Place the capacitor at appropriate location which determine in previous. 13
  • 14. Location and sizing of capacitor determined through GA Location and sizing of capacitor determined through combined approach of LSF-GA 14 7 10 14 135 37.3 12.8 10.745.534.2 4 9 13 5 2 41.4 29.1 12.5 47.4 29.2
  • 15. Location and sizing of capacitor determined through GA Location and sizing of capacitor determined through combined approach of LSF-GA 15 4 22 2 324 43.1 14.7 31.615.141.3 25 17 4 3 2 26.8 25.8 44.7 39.3 42.1
  • 16. Location and sizing of capacitor determined through GA Location and sizing of capacitor determined through combined approach of LSF-GA 16 10 13 30 1129 48.2 46.4 40.139.149.3 15 14 31 10 13 47.4 47.4 48.4 49.2 47.8
  • 17. 17 No. of Capacitor Location Size (kVAr) Losses (kW) Voltage before capacitor Voltage after capacitor Elapsed time (CPU time) in sec 1 9 30.546 13.374 1.049 1.066 148.791385 2 5 25.349 13.315 1.033 1.038 170.202313 9 22.978 1.049 1.061 3 5 24.625 13.274 1.033 1.036 210.280912 9 23.582 1.049 1.061 13 10.192 1.037 1.060 4 9 27.601 13.268 1.049 1.062 214.970340 13 11.644 1.037 1.061 3 19.707 1.030 1.034 5 22.231 1.033 1.035 5 5 20.843 13.259 1.033 1.035 239.528647 10 11.185 1.059 1.065 9 16.006 1.049 1.060 3 20.480 1.030 1.036 13 10.717 1.037 1.062 1. Effect of capacitor placement on the system losses is observed by incrementing the number of capacitors in the system. 2. It is clearly observed that when a single capacitor is placed on bus 9, losses of the system are 13.374 kW however a small reduction in losses is observed when we increase the number of capacitor to 5.
  • 18. 18 No. of Capacitor Capacitor Location Size (kVAr) Losses (kW) Capacitor Location Size (kVAr) Losses (kW) 1 9 24.916 10.533 9 28.065 10.535 2 9 20.476 10.476 9 39.033 10.503 5 19.462 6 17.673 3 9 24.565 10.452 9 35.271 10.476 13 12.165 6 10.792 5 14.227 13 17.377 4 13 10.021 10.418 9 41.600 10.428 3 15.623 6 13.291 9 19.722 13 11.517 5 21.064 3 18.693 5 10 14.753 10.341 9 37.313 10.374 5 18.630 6 22.345 9 12.362 13 22.819 3 15.420 3 17.575 13 10.461 7 16.902 1. Light loading conditions, when the number of capacitors is two, then the location provided by GA is on bus no. 9 and 5 it is bus 9 and bus 6 from LSF calculation and losses under this operating condition are 10.476 and 10.503 from GA and LSF approach. 2. With higher number of capacitors, results obtained through GA are more realistic.
  • 19. 19 No. of Capacitor Capacitor Location Size (kVAr) Losses (kW) Capacitor Location Size (kVAr) Losses (kW) 1 9 34.064 16.231 9 35.583 16.238 2 9 25.447 16.145 9 38.692 16.190 5 22.911 14 15.727 3 13 10.994 16.100 9 45.483 16.114 5 33.737 14 15.385 9 24.359 6 26.424 4 6 13.497 16.056 9 42.078 16.078 13 13.398 14 14.509 10 24.979 6 40.984 9 39.687 13 15.167 5 6 10.095 15.982 9 46.808 15.986 9 44.625 14 15.007 5 28.838 6 48.714 13 11.585 13 14.696 2 33.659 2 49.538 1. It is clearly observed that at base case with no capacitor in the system the losses are 16.329 kW. 2. After placement of five capacitors it reduces to 15.982 kW for first approach and it is 15.986 kW by LSF method. This suggests that location identification through GA is a better choice.
  • 20. No. of Capacitor Capacitor Location Size (kVAr) Losses (kW) Capacitor Location Size (kVAr) Losses (kW) 1 5 48.506 23.990 4 49.781 23.992 2 5 35.156 23.661 4 45.024 23.791 9 35.898 9 42.684 3 2 37.641 23.569 4 44.152 23.717 5 40.609 9 35.520 9 34.040 13 41.805 4 2 39.546 23.500 4 43.790 23.531 5 41.199 9 37.217 9 31.320 13 12.907 13 10.341 5 43.572 5 7 37.325 23.389 4 41.448 23.428 10 12.896 9 29.185 14 34.292 13 12.549 5 45.589 5 47.403 13 10.709 2 29.278 20 1. It is clearly observed from table that at base case with no capacitor in the system the losses are 24.00 kW. After placement of five capacitors it reduces to 23.389kW for first approach and it is 23.428 kW by LSF method. 2. The locations for various capacitors at bus 7, 10, 14,5,13 by GA and 4, 9, 13, 5 and 2 by using LSF. This suggests that location identification through GA is a better choice.
  • 21. 21 1. Above table shows the calculation of Loss Sensitivity factor for IEEE 14 bus system under different loading conditions. 2. The amount of LSF is the indication of the suitable candidate for the placement of shunt capacitors. 14 Bus Loading Condition 10% Redu. 10% Inc. 30% Inc. Loss Sensitivity Factor weak Bus Loss Sensitivity Factor weak Bus Loss Sensitivity Factor weak Bus 1.00 9 1.00 9 0.66 4 0.50 6 0.89 14 0.40 9 0.45 13 0.65 6 0.37 13 0.40 3 0.50 13 0.30 5 0.33 7 0.41 2 0.25 2 0.29 5 0.33 7 0.22 7
  • 22. 0 8 16 24 32 1 2 3 4 5 Losses No.of Capacitor Losses (kW) Base Case Light Load Medium Load (10%) Medium Load (20%) Heavy Load (30%) Heavy Load (40%) 22
  • 23. 0 10 20 30 1 2 3 4 5 losses No. of Capacitor Losses (kW) Base Case Light Load Medium Load (10%) Medium Load (20%) Heavy Load (30%) Heavy Load (40%) 23
  • 24. 0 50 100 150 200 13.25 13.3 13.35 13.4 13.45 Optimal Capacitor Placement By Genetic Algorithm Number of Generation LossesMinimum(kw) 24
  • 25. 25 1. It is clearly observed from the table that when a single capacitor is placed on bus 4, losses of the system were 17.639 kW however a small reduction in losses is observed when we increase the number of capacitor to 5. No. of Capacitor Capacitor Location Size (kVAr) Losses (kW) Voltage before capacitor Voltage after capacitor Elapsed time (CPU time) in sec 1 4 41.909 17.639 1.003 1.030 223.273913 2 4 37.689 17.479 1.003 1.026 240.145030 24 13.335 0.991 1.030 3 4 36.127 17.315 1.003 1.024 254.594486 24 13.425 0.991 1.046 10 33.050 1.014 1.058 4 3 21.665 17.214 1.014 1.038 260.006960 10 38.038 1.014 1.062 24 11.319 0.991 1.048 8 35.953 0.993 1.024 5 24 13.097 17.026 0.991 1.038 294.896335 26 40.204 0.977 1.059 12 22.699 1.046 1.063 10 42.959 1.014 1.049 3 15.308 1.014 1.038
  • 26. 26 1. Light loading conditions, when a single shunt capacitor is placed on bus 21, loss is reduced from 14.02 to 13.869 whereas using LSF is found to be maximum for bus 20 and the loss is reduced to 13.961. 2. When the number of capacitors is two, the losses under this operating condition are 13.790 and 13.867 from GA and LSF approach. No. of Capacitor Capacitor Location Size (kVAr) Losses (kW) Capacitor Location Size (kVAr) Losses (kW) 1 21 14.539 13.869 20 18.154 13.961 2 4 28.674 13.790 20 24.533 13.867 21 12.819 24 18.968 3 24 16.425 13.751 20 20.169 13.787 4 24.830 24 17.274 8 21.276 3 26.675 4 8 22.331 13.722 20 24.606 13.746 21 14.338 24 18.209 4 28.043 3 28.571 19 10.185 21 17.891 5 4 22.922 13.032 20 17.348 13.523 7 13.196 24 15.455 23 10.395 3 28.377 8 21.690 21 17.100 21 14.047 7 16.423
  • 27. 27 1. It is observed that with no capacitor in the system the losses are 22.697kW.After placement of five capacitors it reduces to 21.345 kW for first approach and 21.425 kW by LSF method. 2. The location of shunt capacitors are 21, 7, 8, 4 and 24 determined through GA while 3, 4, 24, 21, and 27 through LSF approach. No. of Capacitor Capacitor Location Size (kVAr) Losses (kW) Capacitor Location Size (kVAr) Losses (kW) 1 4 42.836 21.961 3 44.823 22.100 2 22 20.626 21.776 3 36.833 21.872 4 41.949 4 46.134 3 3 19.850 21.660 3 34.811 21.675 24 14.208 4 46.487 4 42.150 24 16.322 4 24 10.838 21.556 3 37.764 21.563 9 30.173 4 46.292 21 13.076 24 11.455 4 45.893 21 25.102 5 21 18.263 21.345 3 34.938 21.425 7 13.200 4 44.168 8 26.290 24 17.039 4 37.437 21 24.780 24 14.789 27 12.002
  • 28. 28 1. It is observed that with no capacitor in the system the losses are 33.98 kW. After placement of five capacitors it reduces to 31.333 kW for first approach and it is 31.415 kW by LSF method. 2. As number of capacitors increased results obtained through GA are more realistic as size as well as losses calculated by the GA is less than LSF approach. No. of Capacitor Capacitor Location Size (kVAr) Losses (kW) Capacitor Location Size (kVAr) Losses (kW) 1 21 34.159 33.029 25 39.474 33.488 2 4 42.259 32.261 25 42.556 33.011 21 28.360 17 34.292 3 24 23.639 31.908 25 33.332 32.294 3 42.977 17 28.724 7 35.319 4 47.193 4 10 43.915 31.538 25 28.242 31.623 7 32.390 17 26.935 3 44.961 4 48.442 24 17.766 3 47.557 5 4 43.152 31.333 25 26.813 31.415 22 14.724 17 25.863 2 41.356 4 44.740 24 15.118 3 39.399 3 31.628 2 42.150
  • 29. 29 1. Above table shows the calculation of Loss Sensitivity factor for IEEE 14 bus system under different loading conditions. 2. The amount of LSF is the indication of the suitable candidate for the placement of shunt capacitors. 30 Bus Loading Condition 10% Redu. 10% Inc. 30% Inc. Loss Sensitivity Factor weak Bus Loss Sensitivity Factor weak Bus Loss Sensitivity Factor weak Bus 4.30 20 4.00 3 12.0 25 3.00 24 3.70 4 10.8 17 2.60 3 2.80 24 6.00 4 2.20 21 2.00 21 3.00 3 1.50 7 1.60 27 2.46 2 1.30 10 1.50 26 1.59 29
  • 30. 0 8 16 24 32 40 1 2 3 4 5 Losses No.of capacitor Losses (kW) Base Case Light Load Medium Load (10%) Medium Load (20%) Heavy Load (30%) Heavy Load (40%) 30
  • 31. 0 8 16 24 32 40 48 1 2 3 4 5 losses No. of Capacitor Losses (kW) Base Case Light Load Medium Load (10%) Medium Load (20%) Heavy Load (30%) Heavy Load (40%) 31
  • 32. 0 50 100 150 200 17.00 17.026 17.6 17.7 17.8 17.92 Optimal Capacitor Placement By Genetic Algorithm Number of Generation LossesMinimum(kW) 32
  • 33. 33 No. of Capacitor Capacitor Location Size (kVAr) Losses (kW) Voltage before capacitor Voltage after capacitor Elapsed time (CPU time) in sec 1 12 49.027 173.249 0.958 0.987 219.035626 2 12 47.045 167.914 0.958 0.989 227.778733 14 42.895 0.934 0.970 3 10 38.422 163.304 0.961 0.991 239.630848 11 43.340 0.959 0.980 29 43.240 0.969 0.994 4 29 48.276 160.733 0.969 0.991 248.594394 11 47.118 0.959 0.977 22 47.288 0.964 0.992 13 49.650 0.952 0.975 5 21 46.158 156.805 0.965 0.989 260.236893 12 48.171 0.958 0.976 25 43.545 0.963 0.982 22 48.068 0.964 0.984 10 38.806 0.961 0.977 1. It is clearly observed that when a single capacitor is placed on bus 12, losses of the system were 173.249 kW from 178.735. 2. A significant reduction in losses is observed when the number of shunt capacitors is increased up to 5. Location of shunt capacitor is determined by GA.
  • 34. 34 1. Under light loading conditions placement of single capacitor on bus 13 (GA) and bus 15 (LSF) results in reduction of from 141.698 to 136.951 and 137.943. 2. When the number of capacitors is two, then the location is on bus no. 12 and 29 (GA) however it is bus 15 and bus 10 (LSF) with losses under this condition are 133.420 and 134.853 No. of Capacitor Capacitor Location Size (kVAr) Losses (kW) Capacitor Location Size (kVAr) Losses (kW) 1 13 48.867 136.951 15 49.532 137.943 2 12 48.187 133.420 15 48.688 134.853 29 44.590 10 46.116 3 29 38.138 129.460 15 45.936 131.124 12 46.195 10 49.806 13 49.038 14 49.959 4 25 45.269 121.304 15 45.366 128.162 14 46.866 10 49.531 22 49.215 14 48.951 29 45.018 30 46.980 5 13 46.164 120.416 15 49.453 125.980 14 42.324 10 48.717 10 44.166 14 46.238 29 45.949 30 48.784 25 48.024 13 48.957
  • 35. 35 1. It is observed that at base case with no capacitor in the system the losses are 221.196 kW. After placement of five capacitors it reduces to becomes 192.045 kW for first approach and it is 195.651 kW by LSF method. 2. With increase in number of capacitors a significant reduction in losses are evaluated. No. of Capacitor Capacitor Location Size (kVAr) Losses (kW) Capacitor Location Size (kVAr) Losses (kW) 1 12 49.145 214.623 15 49.233 215.969 2 12 46.534 209.470 15 48.663 210.157 13 46.756 13 49.219 3 12 48.823 205.538 15 47.503 206.146 14 47.539 13 48.875 25 41.359 10 44.218 4 22 48.983 193.973 15 48.173 200.720 11 46.989 13 49.727 13 47.657 10 47.663 25 48.332 29 47.995 5 29 47.508 192.045 15 46.353 195.651 12 48.555 13 49.750 30 45.873 10 48.217 14 47.668 29 47.960 11 46.620 12 49.807
  • 36. 36 1. It is observed that at base case with no capacitor in the system the losses are 324.383 kW. After placement of five capacitors it becomes 284.038 kW (GA) and it is 289.386 kW (LSF). 2. The locations for various capacitors at bus 10, 13, 30, 29 and 11 by GA while 15, 14, 31, 10 and 13 through LSF. No. of Capacitor Capacitor Location Size (kVAr) Losses (kW) Capacitor Location Size (kVAr) Losses (kW) 1 12 48.307 315.607 15 49.845 317.143 2 13 49.387 308.030 15 49.400 310.042 11 44.357 14 48.542 3 9 47.540 300.098 15 49.493 307.247 13 48.822 14 49.735 12 45.008 31 47.482 4 11 48.579 294.642 15 47.723 298.390 25 44.020 14 47.409 13 47.357 31 49.939 10 48.423 10 49.599 5 10 48.215 284.038 15 47.475 289.386 13 46.481 14 47.460 30 49.337 31 48.486 29 39.158 10 49.219 11 40.112 13 47.847
  • 37. 37 1. Above table shows the calculation of Loss Sensitivity factor for IEEE 14 bus system under different loading conditions. 2. The amount of LSF is the indication of the suitable candidate for the placement of shunt capacitors. 33 Bus Loading Condition 10% Redu. 10% Inc. 30% Inc. Loss Sensitivity Factor weak Bus Loss Sensitivity Factor weak Bus Loss Sensitivity Factor weak Bus 11.0 15 19.0 15 17.7 15 6.75 10 5.45 13 15.5 14 2.75 14 2.92 10 3.35 31 2.06 30 1.90 29 2.53 10 0.49 13 0.55 12 0.62 13 0.39 32 0.46 32 0.47 30
  • 38. 0 75 150 225 300 375 1 2 3 4 5 Losses No. of Capacitor Losses (kW) Base Case Light Load Medium Load (10%) Medium Load (20%) Heavy Load (30%) Heavy Load (40%) 38
  • 39. 0 75 150 225 300 375 450 1 2 3 4 5 losses No. of Capacitor Losses (kW) Base Case Light Load Medium Load (10%) Medium Load (20%) Heavy Load (30%) Heavy Load (40%) 39
  • 40. 0 50 100 150 200 154 156 158 160 162 164 Optimal Capacitor Placement By Genetic Algorithm Number of Generation LossesMinimum(kW) 40
  • 41. Thus we conclude that with the placement of shunt capacitor in radial distribution system results in 1. Results are calculated for both approaches and compared for different loading conditions. 2. GA optimization technique is effective in deciding the position where different size capacitors to be placed, for different number of candidate buses. 3. GA Search optimization technique generate more superior results than LSF with GA optimization in terms of power loss reduction. 4. Optimal placement and sizing of capacitors give improved voltage profile and higher power loss reduction. 41
  • 42. [1] HadiSaadat, “Power System Analysis”, McGraw-HiII Series in Electrical and Computer Engineering, 1999. [2] Mesut E. Baranand, Felix F. Wu, “Optimal capacitor placement on Radial distribution systems”, IEEE Transactions on Power Delivery, Vol. 4, No. 1, January 1989. [3] S.Sundhararajan, A.Pahwa, “Optimal selection of capacitors for radial distribution systems using a genetic algorithm”,Power Systems, IEEE Transactions on , Vol. 9, No. 3, pages 1499-1507, August 1994. [4] Hsiao-Dong Chiang, Jin-Cheng Wang, Jianzhong Tong,G. Darling, “Optimal capacitor placement, replacement and control in large-scale unbalanced distribution systems: system solution algorithms and numerical studies”,Power Systems, IEEE Transactions on , Vol. 10, No.1, pages 363-369, February 1995. [5] Hong-TzerYang, Yam-Chang Huang, Ching-Lien Huang, “Solution to capacitor placement problem in a radial distribution system using tabu search method”,Energy Management and Power Delivery, International Conference, Vol. 1, pages 388-393, November 1995. [6] M.H.Haque, “Capacitor placement in radial distribution systems for loss reduction”, IEEE General Transmission Distribution,Vol. 146, No. 5, September 1999. 42
  • 43.  [7] D.Richardson, “Identification of Capacitor Position in a Radial System”, IEEE Transactions on Power Delivery, Vol.14, No.4,pages 1368-1373, October 1999.  [8] J. C. Carlisle, A. A. El-Keib, “ A Graph Search Algorithm for Optimal Placement of Fixed and Switched Capacitors on Radial Distribution Systems”, IEEE Transactions on power delivery, Vol. 15, No. 1, January 2000.  [9] A.Augugliaro, L. Dusonchet, E.R. Sanseverino, “An Evolutionary Parallel Tabu Search approach for distribution systems reinforcement planning”, Advanced Engineering Informatics, Vol. 16, pages 205–215, July 2002.  [10] Young-Jae Jeon, Jae-Chul Kim, “Application of simulated annealing and tabu search for loss minimization in distribution systems”, Electrical Power and Energy Systems, Vol. 26, January 2004.  [11] M. A. S. Masoum, M. Ladjevardi, E. F. Fuchs, W. M. Grady, “Application of local variations and maximum sensitivities selection for optimal placement of shunt capacitor banks under non sinusoidal operating conditions”, Int. J. Electrical Power Energy System, Vol. 26, No. 10, pages.761–769, 2004.  [12] Ying-Tung Hsiao, Chia-Hong Chen, Cheng-ChihChien, “Optimal capacitor placement in distribution system using a combination fuzzy–GA method”, Electrical Power and Energy Systems, Vol. 26, pages 501-508, September 2004. 43
  • 44.  [13] Luis Rojas, Rodolfo Garcia, Luis Roa, “Optimal Capacitor Location for Radial Systems using Genetic Algorithms”,Transmission & Distribution Conference and Exposition 2006. TDC '06. IEEE/PES , Vol. 1, No. 4, pages 15-18, August 2006.  [14] D. Das, “Optimal capacitor placement in radial distribution system using fuzzy-GA method”, Electrical Power and Energy Systems, Vol. 30, pages 361-367, August 2007.  [15] V.V.K. Reddy, M. Sydulu, “Index and GA based Optimal Location and Sizing of Distribution System Capacitors”,Power Engineering Society General Meeting 2007. IEEE, Vol. 1, No. 4, pages 24-28, June 2007.  [16] A. Lakshmi Devi, B. Subramanyam, “Optimal dg unit placement for loss reduction in Radial distribution system-a case study”, ARPN Journal of Engineering and Applied Sciences, Vol. 2, No. 6, December 2007.  [17] R.A. Jabr,“Optimal placement of capacitors in a radial network using conic and mixed integer linear programming”, Electric Power Systems Research, Vol. 78, June 2008.  [18] M. Damodar Reddy, V.C. Veera Reddy, “Optimal capacitor placement using fuzzy and Real Coded Genetic Algorithm for maximum saving”, Journal of Theoretical and Applied Information Technology, 2008. 44
  • 45.  [19] Ahmad Galal Sayed, Hosam K.M. Youseef, “Optimal sizing of fixed capacitor banks placed on a distorted interconnected distribution networks by Genetic Algorithms”,Computational Technologies in Electrical and Electronics Engineering 2008. IEEE Region 8 International Conference, July 2008.  [20] MarvastiVahid, M. Manouchehr, N. Hossein, S.D. Jamaleddin, “Combination of optimal conductor selection and capacitor placement in radial distribution systems for maximum loss reduction”,Industrial Technology 2009. ICIT 2009.IEEE International Conference, Vol. 1, No.5, pages 10-13, February 2009.  [21] T. SamimiAsl, S. Jamali, “Optimal capacitor placement size and location of shunt capacitor for reduction of losses on distribution feeders”,Clean Electrical Power, International Conference on, pages 223-226, June 2009.  [22] IB Mady,“Optimal sizing of capacitor banks and distributed generation in distorted distribution networks by genetic algorithms”,Electricity Distribution - Part 1, 2009. CIRED 2009, 20th International Conference and Exhibition, Vol. 1, No. 4, pages 8-11, June 2009.  [23] J. B. V. Subrahmanyam, “Optimal Capacitor Placement in Unbalanced Radial Distribution Network”, Journal of Theoretical and Applied Information Technology, Vol. 6, pages 106-115, 2009. 45
  • 46.  [24] S.M. Hakimi, M. Zarringhalami, S. M. MoghaddasTafreshi , “Optimal capacitor placement and sizing in non-radial distribution to improve power quality”,14th International Conference, Vol 1, No. 6, pages 26-29, September 2010.  [25] Anil Swarnkar,Nikhil Gupta, K. R. Niazi, “Optimal placement of fixed and switched shunt capacitors for large-scale distribution systems using genetic algorithms”,Innovative Smart Grid Technologies Conference Europe (ISGT Europe), 2010 IEEE PES, Vol. 1, No. 8, pages 11-13 October 2010.  [26] HamidRezaSalehi,AliRezaVahabzadeh,HosseinAskarianabyaneh,ForoughMahmoodi anfard, “Optimal capacitor placement for loss reduction”, Modern electric Power System, 2010  [27]Seyed Abbas Taher, Mohammad Hasani, Ali Karimian, “A novel method for optimal capacitor placement and sizing in distribution systems with non linear loads and DG using GA”, Common Nonlinear SciNumerSimulat, Vol. 16, pages 851- 862,February 2011.  [28] Jiachuan Shi, Chunyi Wang, Peng An, “Loop-Based Coding Reactive Tabu Search for Comprehensive Optimization in Distribution Networks”,Power and Energy EngineeringConference (APPEEC) 2011 Asia-Pacific, Vol. 1, No. 4, pages 25-28, March 2011. 46
  • 47.  [29] Macros A.N. Guimaraes, Carlos A. Castro,“An efficient method for distribution systems reconfiguration and capacitor placement using a Chu-Beasley based genetic algorithm”,PowerTech, 2011 IEEE Trondheim, Vol. 1, No. 7, pages 19-23, June 2011.  [30] Y. Mohamed Shuaib, C.ChristoberAsirRajan, “Capacitor Sizing and Placement on Radial Distribution System Using Queen Bee Assisted Genetic Algorithm”,Process Automation, Control and Computing (PACC), 2011 International Conference, Vol. 1, No. 8, pages 20-22, July 2011.  [31] S.Neelima, Dr. P.S.Subramanyam, “Optimal capacitor placement in distribution system using Genetic Algorithm: A Dimension Reducing Approach”, Journal of Theoretical and Applied Information Technology, Vol.30, No.1, August 2011.  [32] Majid Davoodi, Mohsen Davoudi, IrajGanjkhany, Ali Aref, “Optimal Capacitor placement in Distribution Networks Using Genetic Algorithm”, Vol. 5 ,July 2012.  [33] Deepti Sharma, AmitaMahor, “Optimal Placement of Capacitor in Radial Distribution System Using Real Coded Genetic Algorithm”, International Journal of Electrical Engineering, August2013. [34] Attia A. El-Fergany, “Involvement of cost savings and voltage stability indices in optimal capacitor allocation in radial distribution networks using artificial bee colony algorithm”, International Journal of Electrical Power & Energy Systems, Vol. 62, Pages 608-616, November 2014.  [35] www.mathworks.com 47
  • 48. 48