Energy and Power Engineering, 2011, 3, 508-512
doi:10.4236/epe.2011.34061 Published Online September 2011 (http://www.SciRP.org/journal/epe)
Optimization of Recloser Placement to Improve Reliability
by Genetic Algorithm
Nematollah Dehghani1, Rahman Das hti 2
1Young Researchers Club of Islamic Azad University, Bushehr Branch, Bushehr, Iran
2Department of Electrical and Computer Engineering, Islamic Azad University, Bushehr Branch, Bushehr, Iran
Received April 23, 2011; revised May 27, 2011; accepted June 11, 2011
Abstract
In this paper, a simple method for placing an optimal number of recloser is presented. The algorithm is
solved using genetic algorithm as the optimization method. The majority of outage events experienced by
customers are due to electrical distribution failures. Increasing network reliability is a necessity in order to
reduce interruption events. Distribution network automation can trim down outage events and increase sys-
tem reliability. Network automation has to be done using optimization approaches. Genetic Algorithm (GA)
is a relatively new technique used in power systems optimization problems. Distribution network automation
is one of the aspects tackled using GA. Howe ver , the methodologies used to improve the reliability of radial
distribution feeders are reviewed. The reliability improvement is demonstrated for typical distribution feeder
layouts determined. The method enjoys the simplicity of configure uration, accuracy of the results and re-
duction of the time consuming. The obtained results also show the applicability of the algorithm.
Keywords: Recloser, Reliability, Transient Error, GA
1. Introduction
The necessity to provide reliability in the distribution
system and in power can be defined as the system’s ca-
pability in generation, transmission and distribution of
electricity energy to the consumers. In the distribution
part, halt frequent (frequency of occurrence of halts and
interrupts), the time period of each halt and the rate of
consumption each consumer needs in the lake of con-
sumer service and lake of feeding system are the main
factor s to judge about systems reliab ility.
Many factors are important in the determining the re-
liability of a system that some of them are controllable.
These factors are depend on variable such as the reliabil-
ity of the equipment components, networks length its
loading, networks configuration loads characteristic.
Other factor are also important in reduction of network
s reliability such as disorder and distribution by third
part ,atmospheric and environmental condition like tem-
perature, humidity ,environmental, and air pollution, wind,
rain, rain, snow ,ice, and thunderbolt.
Type of Errors in Power System
Generally, errors created in the electricity system in the
term of cause and time of the error continuation are two
section inside and outside errors.
Considering the above, short circuit is derided to pas-
sive short circuit and permanent short circuit. Whereas
about 80 percentage of error in medium voltage distribu-
tion system is passive and elimination after one or two
time switching of station after automatic interrupts. That
is a try and error performance to detect of the errors that
causes sever stress and creation of special transient
modes in network and also causes the reduction of sta-
tion’s key life time. so, in order to prevent unsuccessful
switching from transmission and high distribution system
and to increase from transmission and high distribution
systems and to increase the reliability in transmission of
electrical energy and establishing its continuity and also
creating stability in system of medium voltage lines, a
soft were installation called recloser is required that
should meet following cases:
• No repeated switching of station in transient errors.
• No switching off of the loads because of lines prob-
lem in one region.
• Considering the above, the place of recloser’s instal-
lation and the aims witch the place of installation is
determined has specific importance. There are two
views in the positioning of recloser system.
• Recloser’s positioning from the view of cost’s reduc-

N. DEHGHANI ET AL.
509
tion.
• Recloseer’s positioning in order to reduce transient
interruption and reduction of undistributed energy
and satisfaction of customers.
This report deals with second view of recloser posi-
tioning that is reduction of transient interruptions and
thus reduction in undistributed energy and satisfaction of
customers .
This paper concern with the collection of total average
frequency of occurred errors in the region.
2. Genetic Algorithm
A Genetic algorithm (GA) is an optimization based on
the mechanics of natural selection and natural genetic.
GA is inspired by natural genetics and a Darwinian the-
ory of evolution. A genetic algorithm involves simulat-
ing competition between a numbers of individuals who
represent solution to problem. A set of genes which cor-
responds to a “chromosome” in natural genetic is re-
ferred to as “string” in a GA [1,2]. The mechanics of
GAs are surprisingly simple, involving nothing more
complex than copying string and swapping partial string.
GAs start with a population of string and thereafter, gen-
erate successive population using the following three
basic operator generation, crossover, mutation [3]. Con-
cisely stated, a genetic algorithm is a programming tech-
nique that mimics biological evolution as a prob-
lem-solving strategy. Given a specific problem to solve,
the input to the GA is a set of potential solutions to that
problem, encoded in some fashion, and a metric called a
fitness function that allows each candidate to be quantita-
tively evaluated [4]. These candidates may be solutions
already known to work, with the aim of the GA being to
improve them, but more often they are generated at ran-
dom. The GA then evaluates each candidate according to
the fitness function. In a pool of randomly generated
candidates, of course, most will not work at all, and these
will be deleted. However, purely by chance, a few may
hold promise - they may show activity, even if only weak
and imperfect activity, toward solving the problem.
These promising candidates are kept and allowed to re-
produce. Multiple copies are made of them, but the cop-
ies are not perfect; random changes are introduced dur-
ing the copying process. These digital offspring then go
on to the next generation, forming a new pool of candi-
date solutions, and are subjected to a second round of
fitness evaluation. Those candidate solutions which were
worsened, or made no better, by the changes to their
code are again deleted; but again, purely by chance, the
random variations introduced into the population may
have improved some individuals, making them into bet-
ter, more complete or more efficient solutions to the
problem at hand. Again these winning individuals are
selected and copied over into the next generation with
random changes, and the process repeats. The expecta-
tion is that the average fitness of the population will in-
crease each round, and so by repeating this process for
hundreds or thousands of rounds, very good solutions to
the problem can be discovered. As astonishing and
counterintuitive as it may seem to some, genetic algo-
rithms have proven to be an enormously powerful and
successful problem-solving strategy, dramatically dem-
onstrating the power of evolutionary principles. Genetic
algorithms have been used in a wide variety of fields to
evolve solutions to problems as difficult as or more dif-
ficult than those faced by human designers [5]. Moreover,
the solutions they come up with are often more efficient,
more elegant, or more complex than anything compara-
ble a human engineer would produce. In some cases,
genetic algorithms have come up with solutions that baf-
fle the programmers who wrote the algorithms in the first
place!
3. Reliability Characters
In a conventional radial feeder, reclosers are only ex-
pected to detect the unidirectional flow of current. Typi-
cally, a recloser upstream from the fault location detects
the fault current, trips, and goes into a predefined reclos-
ing sequence in order to restore service, if the fault is of a
temporary nature. If more reclosers are present on the
radial feeder, they are time coordinated, usually using
Inverse Definite Minimum Time (IDMT) curves [6].
IDMT allows for the recloser operating time to be in-
versely proportional to the magnitude of the fault current,
forcing the recloser closest to the fault to operate first
and clear the fault.
The placement of protection devices in a conventional
(radial) feeder is designed to maximize network reliabil-
ity, and therefore minimize the traditional reliability in-
dices assuming that the energy source is located only at
the substation. Typically, utilities use standardized indi-
ces such as SAIFI and SAIDI, which measure the average
accumulated duration and frequency of sustained inter-
ruptions per customer [7,8]. The system average inter-
ruption frequency index (SAIFI) and the system average
interruption duration index (SAIDI) are defined as fol-
lows:
ii
T
N
SAIFI N
λ ⋅
=
∑
(1)
ii
T
rN
SAIDI N
⋅
=
∑
(2)
where Ni is the number of interrupted customers for each

N. DEHGHANI ET AL.
510
interruption event, NT total number of customers, and ri
is the restoration time for each interruption event. Cus-
tomer average interruption duration index (CAIDI) is
defined as the average time required to restore service to
the average customer per sustained interruption:
ii
ii
rN SAIDI
CAIDI N SAIFI
λ
⋅
= =
⋅
∑
∑
(3)
The average service availability index (ASAI), repre-
sents the fraction of time that a customer has power pro-
vided during one year (or other defined reporting period).
Assuming one year period (8760 hours), it is calculated
via:
8760
8760
SAIDI
ASAI − =
(4)
As the importance of temporary faults increases, more
utilities are starting to track them using the MAIFIe index,
which measures the number of momentary interruptions
per customer.
The momentary average interruption event frequency
index (MAIFIe) is defined
ii
T
ID N
MAIFIe N
⋅
=
∑
(5)
where IDi is the number of interrupting device operations.
The recloser placement can be optimized with respect
to any of these, or some other, indices. To include the
effects of both sustained and momentary interruptions, a
composite index may be used, as defined below.
12
3
TT
TT
T
T
SAIFI SAIFISAIDI SAIDI
CW W
SAIFI SAIDI
MAIFIe MAIFIe
WMAIFIe
−−

= +



−
+


where W1,W2 and W3 are weights for indices SAIFI,
SAIDI, and MAIFIe, respectively, and the subscript T
indicates the target value [9].
The percentage of different companies imply different
variables of reliability shown in Figure 1 [10].
4. Problem Definition Using
In this stage after importing system’s information n bit of
0, 1 is considered equal to the number of lines candidate
for recloser (Figure 2).
Genetic algorithm concerns with generation of differ-
ent gene. Calculation s are based on a system of genes
generation. If the I bit value of 1 (that is between I and
i-1 line would be without recloser and has no effect on
calculatio n. It should be noted that found to be not suit-
able for recloser by experience, could be locked perma-
Figure 1. Percentage of companies using indices (49 compa-
nies responded).
nently though genetics toolbox like the most of terminal
shins.
5. The Effects of Network Simulation Result
on the Standard System in Order to
Checking Variables
For testing the program, first it should be evaluated by
the standard testing system (RBTS) in Figure 3.
The information is shown in Tables 1 and 2 and the
result is shown in Table 3.
6. Simulation Result on the Standard Power
System
Reference number [5] suggests placing a recloser at the
half-way point of the radial feeder, assuming uniformly
distributed load. This would yield a 50% reliability im-
provement to customers upstream from the recloser.
Similarly, locations at 1/3 and 2/3 of feeder length
should be considered if two reclosers are to be placed. In
1
...
0
1
0
...
0
1
n-bit for recloser candid lines
Figure 2. Coding of variable. N: the number of communica-
tive lines between shins.
Figure 3. Standard test power system (rbts). Lp: the poin of
load (load point).

N. DEHGHANI ET AL.
511
Table 1. Data of estandard feeder—RBTS-ODD SEC-
TIONS.
Main section Le ng t h (km) λ (f/y r) r (hr /f) S (hr /f)
1 0.75 0.04875 5 1
3 0.60 0.03900 5 1
5 0.75 0.04875 5 1
7 0.75 0.04875 5 11
9 0.60 0.03900 5 1
11 0.80 0.05200 5 1
Table 2. data of standard feeder—RBTS-EVEN SEC-
TIONS.
La ter al
section
Lengt h
(km )
λ
(f/yr) R (hr/f) S
(hr/f )
Tra nsformer
λ
(f/y )
rl
(hr/f )
2 0.60 0.03900 5 1 13 0.015 10
4 0.80 0.0520 0 5 1 14 0.015 10
6 0.75 0.04875 5 1 15 0.015
10
8 0.60 0.03900 5 1 16 0.015
10
10 0.75 0.04875 5 1 17 0.015
10
12 0.65 0.03900 5 1 18 0.015
10
real life situations, which include the presence of critical
loads and non-uniform load distributions, utilities often
resort to engineering judgment to place reclosers ac-
cording to the reliability guidelines.
In this paper we using genetic algorithm to find best
position for recloser. This is important step using GA for
optimal allocation recloser in power system with 70 bus
Figure 4.
This part is concern with optimal positioning of re-
closer by the method that is used in the system of Figure
4 and its result are compared with reference [5], Table 3
shows comparing result.
7. Final Simulation Result
All simulation is done in MATLAB software environ-
ment . The number of population member and the number
of generation is chosen for solving the question.
Crossover probability is set to 0.8 mutation probability
is set as following At first, completing algorithm starts
from 0.1and then reduces in each generation and at last
gets to 0.001 (Table 4).
It could be seen that reliability is increased in large
amount.
Figure 4. 70-bus test power system.
Table 3. Comparing result.
Reliability
cha ra cters
Result in
objective function
by GA
Result in
reference [5]
SAIFI 0.3355 0.3355
SAIDI 0.8328 0.8328
Table 4. Final simulation result by GA.
Position of
recloser
Final result in
refrence [5]
Final result
in objective
function by
GA
Number of
recloser
8 - 9 3.9560 3 .5424 1
8 - 9 ,
30 - 31 2.8 695 2.0847 2
3 - 4, 30 -
31, 47 - 48 1.9 012 1.1937 3
8. Conclusions
In this paper we study one of the protective device in
electrical power system ,this device can recognize of
unusual states for example transient errors can do best
activity ,other than the passing fault have important rule
in position and act of recloser.
This paper has presented an effective application of
the genetic algorithm optimization to practical distribu-
tion system automation. One location may satisfy an ob-
jective function subject to a specific reliability index.
However, adding reliability index as a constraint can

N. DEHGHANI ET AL.
512
result in different optimum locations. Multiple reliability
indices were combined to form an objective function
using the concept of a composite index in which the
weights and target values of each index must be deter-
mine d .
Genetic Algorithm optimization has been shown to be
an effective technique to optimize the automation of
some electrical distribution systems as has been illus-
trated.
This paper is studying the networks which encounter
high time outage due to the transient faults. After pre-
senting the GA for optimal allocating of recloser and it’s
simulation on a 70 buses network, we can improve reli-
ability of system and after that we comparing result with
reference [5] and find GA is best method to optimiza-
tion .The results demonstrate the capability and applica-
bility of the proposed method.
9. Suggestion
1) In addition to finding problematic lines and prioritiza-
tion of them according to sensitive and important places,
using and installing the recloser requires and important
places, using and installing the recloser requires suitable
installation place and correct setting with regard to re-
closer facilities, in order to have return on capital equal
to customers satisfaction.
Oth er wise , not only the customers satisfaction would
not be achieved, but also, huge costs will be imposed on
distribution company.
2) Installation and setting of recloser should be done
by expert.
3) If using the recloser, their subsidiary equipment
should be reviewed and mentioned in order.
Obtain more economic interests through optimal using
of them.
4) These studies show that, to be more economic in
using recloser, not only there installation and commis-
sioning is important but also several studies should be
done about feeders and their type of load to get accept-
able results.
10. References
[1] D. E. Goldberg, “Genetic Algorithms in Search, Optimi-
zation and Machine Learning,” Addison-Wesley, Long-
man, London, 1998
[2] O. Pavel, “Optimization of Parameters of Fuzzy Control-
lers by Genetic Algorithm,” Technical University of
Berno, Faculty of Mechanical Engineering, Borno, 2010.
[3] E. M. Rudinck, J. H. Patel, G. S. Greenstein and T. M.
Niermann, “Sequential Circuit Test Generation in Genetic
Algorithm Framework,” Proceedings of ACM/IEEE De-
sign Automation Conference, San Diego, 6-10 June 1994,
pp. 698- 704.
[4] M. Kalanter, R. Dashti and R. Dashti, “Combination of
Network. Recon Figuration and Capacitor Placement for
Loss Reduction in Disterbution System with Based Ge-
netic Algorithm,” UPEC Conference, New Castle, 6-8
September 2006.
[5] Z. Feng, “Electric Distribution System Risk Assessment
Using Actual Utility Reliability Data,” Thesis, March
2006, pp. 20-24.
[6] A. R. Bergen, “Power System Analysis,” 1st Ed ition,
Prentice-Hall, Upper Saddle River, 1986.
[7] IEEE, “IEEE Trial-Use Guide for Electric Power Distri-
bution Reliability Indices,” IEEE 1366-1998 Standard,
1998
[8] C. A. Warren, R. Ammon and G. Welch, “A Survey of
Distribution Reliability Measurement Practices in the
US,” IEEE Transactions on Power Delivery, Vol. 14, No.
1, 1999, pp. 250-257. Udoi :10.1109/61.736733U
[9] A. Pregelj, “Impact of Distributed Generation on Power
Network Operation,” Ph.D. Thesis, School of Electrical
and Computer Engineering, Georgia Institute of Tech-
nology, Atlanta, 2003.
[10] T. Guonen, S. M. Rezaei, “Distribution Engineering,”
Tehran University Publishing, Tehran, No. 1-1375