Application of Gravitational Search Algorithm and Fuzzy For Loss Reduction of Networked System Using Distributed Generation
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This document presents a two-stage methodology for optimizing the location and sizing of distributed generation (DG) resources in transmission and distribution systems to reduce losses. The first stage uses a fuzzy inference system to determine optimal DG locations based on loss and voltage indices. The second stage uses a gravitational search algorithm to compute the optimal DG sizes at the identified locations to minimize losses. The proposed approach is tested on IEEE 5-bus, 14-bus, and a 62-bus Indian practical system, demonstrating reductions in losses and improvements in voltage profiles.
![IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE)
e-ISSN: 2278-1676,p-ISSN: 2320-3331, Volume 10, Issue 1 Ver. II (Jan – Feb. 2015), PP 33-37
www.iosrjournals.org
DOI: 10.9790/1676-10123337 www.iosrjournals.org 33 | Page
Application of Gravitational Search Algorithm and Fuzzy For
Loss Reduction of Networked System Using Distributed
Generation
K. Dhananjaya Babu1
, Dr. A. Lakshmi Devi2
1
(Research Scholar, Department of EEE, SVU College of Engineering, SV University, Tirupati, India)
2
(Professor, Department of EEE, SVU College of Engineering, SV University, Tirupati, India)
Abstract: The insertion of distributed generator (DG) into distribution systems for performance improvement
has been an obvious application. But the choice of DG has been extended to the networked systems as well in
the recent years. The penetration level of DG is accelerating due to the liberalization of electricity markets and
Technological advances in small generators and energy storage devices. In this paper the DG technology is
used for loss reduction in the networked system. Functioning of DG is effective not merely on DG itself, but
through proper location and size of the same. In this paper optimal magnitude of power from DG is computed
by using a new algorithm (i.e. Gravitational Search Algorithm (GSA)), upon finding optimal locations through
fuzzy inference system. The proposed combination of GSA and Fuzzy concept is tested on an IEEE 5 bus system,
IEEE 14 bus system and 62-bus practical Indian system.
Keywords: Distribution Generation, Fuzzy Approach, Gravitational Search Algorithm
I. Introduction
Distributed Generation(DG) is utilised a power supply in distribution systems which was extended to
power transmission system for - choking transmission loss, improving voltage profile, and reducing the
congestion etc. The integration of DG with the utility system at permissible voltage involves usage of small
scale distributed energy resources. DG mainly constitutes non-conventional and renewable energy sources like
solar PV, wind turbines, etc. [1]
DG involves the interconnection of small-scale distributed energy resources with the main power utility
at distribution voltage level [2-4]. DG can reduce power loss and can improve node voltages. Further power loss
lowering can be at expense of worse voltage profile and vice versa [5]. These two main outcomes should be
compromised to get an optimal overall performance. Here these effects are highly dependent on DG allocation
in the transmission or distribution system. So the sizing and locations of DG have to be computed carefully to
optimize the overall Performance, which will result in technical and economic benefits [6-7].
In this paper section II describes the Fuzzy approach for DG optimal locations which rely on loss and
voltage indices. Section III describes the methodology for DG sizing using GSA. Section IV presents the result
analysis and discussion for the proposed concept.
II. Fuzzy Approach For Optimal DG Locations
Fuzzy logic is a technique that allows for quantification and processing of common language rules to
arrive at a decision. Al1 the rules are considered at once or in parallel to arrive at a weighted decision [8-9]. In
this paper the basics of applying the fuzzy logic method is for finding optimal locations of DG. Newton-raphson
method is used for load flow study, which was applied on IEEE 5 bus system, IEEE 14 bus system, and 62 bus
Indian practical systems.
2.1 Loss index
In this index the real power loss reduction is observed when total load is completely or partially
removed at a particular load bus. The loss index is determined using (1).
LI = P1
- P2
(1)
Where,
LI = Loss Index
P1
= Power loss for normal load
P2
= Power loss for partial load
The above obtained loss reductions are then, linearly normalized into a [0, 1] range with the largest loss
reduction having a value of 1 and the smallest one having a value of 0. The normalised loss index is given as
one of the two indexes to the fuzzy inference system.](https://image.slidesharecdn.com/e010123337-151119085508-lva1-app6892/85/Application-of-Gravitational-Search-Algorithm-and-Fuzzy-For-Loss-Reduction-of-Networked-System-Using-Distributed-Generation-1-320.jpg)
![Application of Gravitational Search Algorithm and Fuzzy for Loss Reduction …
DOI: 10.9790/1676-10123337 www.iosrjournals.org 34 | Page
Figure 1: One line diagram of Indian 62-bus practical system
2.2 Voltage Index
This index is taken as a second input to the fuzzy inference system, here the voltages are also
normalized into a [0, 1] range with the largest voltage having a value of 1 and the smallest one having a value of
0.
Figure 2: Block diagram of Fuzzy inference system
Rule base and the membership function for the inference system shown in Fig. 2 were adopted from
[10]. The DG location index obtained from the inference system will be considered as optimal locations for DG
in priority wise i.e. index nearing 1 will be best, and for 0 it will be worst case.
III. Methodology For Calculating Sizing Of DG
Gravitational Search Algorithm (GSA) is proposed by Esmat Rashedi et al in 2009[11], which is rely
on law of gravity and mass interactions. In this algorithm, the searcher agents are a collection of masses which
interact with each other based on the Newtonian gravity and the laws of motion.
In GSA, each position of mass (agent) has a solution, wherein the position of heaviest mass has
optimum solution which attracts other masses, by proper acceleration of their gravitational and inertia masses.
At the end iteration count the heaviest mass will present an optimum solution in the search space. In this study
GSA is used for optimizing the capacity of DG by randomly generating the masses between prescribed limits.
Algorithm for DG sizing using GSA
Step 1: Initially [nom x n] number of masses are generated randomly within the limits, where ‘nom’ is the
population size and ‘n’ is the number of DG units. Each row represents one possible solution to the optimal DG-
sizing problem.
Step 2: Similarly [nom x n] number of initial velocities is generated randomly between the limits. Iteration
count (T) is set to one.
Step 3: Calculate the force on the ith
mass by the remaining other masses using (2)
Fij = G×
Mp× Ma
Rij+ ε
× DGxj
d
- DGxi
d
(2)](https://image.slidesharecdn.com/e010123337-151119085508-lva1-app6892/85/Application-of-Gravitational-Search-Algorithm-and-Fuzzy-For-Loss-Reduction-of-Networked-System-Using-Distributed-Generation-2-320.jpg)
![Application of Gravitational Search Algorithm and Fuzzy for Loss Reduction …
DOI: 10.9790/1676-10123337 www.iosrjournals.org 35 | Page
Where,
Maj = Active gravitational mass related to agent j, Mpi = Passive gravitational mass related to agent i,
G = Gravitational constant,
ε = Small constant, and
Rij = Euclidian distance between two agents i and j:
Rij = DGxi,DGxj 2
(3)
The total force that acts on ith
mass in a dimension d be a randomly weighted sum of dth
components of the
forces exerted from other agents.
Fi
d
= randi. Fij
dN
i=1, j ≠ i (4)
Where, rand = random number generated between 0 and 1.
Step 4: After finding the net force on each mass due to the other masses, the acceleration for each mass is
calculated using (5)
ai
d
=
Fi
d
Mi
(5)
Step 5: The velocities and position of all the masses are updated using (6), (7)
Vi
d
= randi × Vi
d
+ ai
d
(6)
DGxi
d
= DGxi
d
+ Vi
d
(7)
Step 6: Fitness values of all the masses are calculated
Step 7: Set error tolerance (e) = 0.0001, if ‘e’ is greater than the difference of best of fitness matrix and mean of
fitness matrix, then go to Step 8, else go to Step 9.
Step 8: The current iteration (T) count is incremented and if ‘T’ is not reached maximum then go to step 3
Step 9: The heaviest mass in the population gives the best fitness value i.e. maximum loss reduction and
position of that mass gives the optimal DG sizes.
IV. Result And Discussion
The proposed concept which is a two – stage methodology has applied on standard IEEE systems as
well on Indian practical system.
IEEE 5 bus system [12] contains 1 slack/generator buses (bus numbers: 1,), 4 load buses (bus numbers:
2, 3, 4, and 5) and 7 transmission lines. Fuzzy approach gives 3rd
location as optimal choice, where GSA
optimized the sizing of DG with which the losses in the system has reduced significantly and voltage profile has
increased significantly.
Figure 3: Voltage profile before and after DG placement for IEEE 5 bus system](https://image.slidesharecdn.com/e010123337-151119085508-lva1-app6892/85/Application-of-Gravitational-Search-Algorithm-and-Fuzzy-For-Loss-Reduction-of-Networked-System-Using-Distributed-Generation-3-320.jpg)
![Application of Gravitational Search Algorithm and Fuzzy for Loss Reduction …
DOI: 10.9790/1676-10123337 www.iosrjournals.org 36 | Page
Figure 4: Voltage profile before and after DG placement for IEEE 5 bus system
Figure 5: Voltage profile before and after DG placement for Indian 62 bus practical system
Table 1: Total Loss before and After Placement of DG using GSA
Test System Used
Optimal Location
by Fuzzy
Approach
Total Loss Before
DG Placement
(MW)
Iterations
generated
Optimized
Capacity of DG
(MW)
Total Loss After
DG Placement
(MW)
IEEE 5 bus
system
3 4.5868 32 108.0316 1.3231
IEEE 14 bus
system
5 13.3934 33 178.8503 5.2879
Indian 62 bus
Practical system
11,
30
64.0512 30
101.7489,
76.9365
60.2518
IEEE 14 bus system [13] contains 5 generator buses (bus numbers: 1,2,3,6 and 8), 9 load buses (bus
numbers: 4, 5, 7,9,10,11,12,13 and14) and 20 transmission lines. Fuzzy approach gives 5th
location as optimal
choice, where GSA optimized the sizing of DG with which the losses in the system has reduced significantly
and voltage profile has increased considerably.
Indian 62 bus practical system [14] contains 19 generator buses (bus numbers: 1, 2, 5, 9, 14, 17, 23, 26,
32, 33, 34, 37, 49, 50, 51, 52, 54, 57, and 58), the remaining are load buses and 89 transmission lines. Fuzzy
approach gives 11th
and 30th
location as optimal choice, where GSA optimized the sizing of DG in both the
locations with which the losses in the system has reduced considerably and voltage profile has increased a little
on some buses.
V. Conclusion
The proposed concept has been successfully applied on IEEE 5 bus, IEEE 14 bus and Indian practical
system. The Fuzzy approach has given best locations (based on index calculated), for installation of DG. For
sizing of DG the Gravitational Search Algorithm optimized accurately for best loss reduction and better voltage
profile.
When compared the results obtained for IEEE standard systems and Indian practical systems, the
effective ness of DG on both the systems is not same, because the real systems are complicated having more
uneven transmission lines with different loading conditions. To some extent, application of DG is a good option
for loss reduction in real system.](https://image.slidesharecdn.com/e010123337-151119085508-lva1-app6892/85/Application-of-Gravitational-Search-Algorithm-and-Fuzzy-For-Loss-Reduction-of-Networked-System-Using-Distributed-Generation-4-320.jpg)
![Application of Gravitational Search Algorithm and Fuzzy for Loss Reduction …
DOI: 10.9790/1676-10123337 www.iosrjournals.org 37 | Page
Every optimization technique when verified on standard test functions will converge perfectly
irrespective of time consumed. A optimization is said to be a better one based on the computation time required,
here GSA is a best technique which take less time, because the masses are updated by taking the cumulative
difference of all masses available, whereas in standard PSO, Firefly Algorithm etc. each particle is been updated
with reference to the global best practical.
References
[1]. Thomas Ackermann , Goran Andersson , Lennart Soder ,Distributed generation: a definition, Electric Power Systems Research 57
(2001) 195–204
[2]. Gopiya Naik S, D. K. Khatod and M. P. Sharma, Optimal Allocation of Distributed Generation in Distribution System for Loss
Reduction, 2012 IACSIT Coimbatore Conferences IPCSIT vol. 28 (2012) © (2012) IACSIT Press, Singapore
[3]. Thomas E Hoff, Howard J Wenger, and Brian K Farmer, Distributed generation An alternative to electric utility investments in
system capacity, Energy Policy. Vol. 24. No. 2. pp. 137-147. 1996
[4]. Naresh Acharya, Pukar Mahat, N. Mithulananthan An analytical approach for DG allocation in primary distribution network,
Electrical Power and Energy Systems 28 (2006) 669–678
[5]. Lucian Ioan Dulau, Mihail Abrudean, Dorin Bica, Effects of distributed generation on electric power systems, The 7th international
conference interdisciplinary in engineering 2003, Procedia technology
[6]. Francesco Gullı´, Small distributed generation versus centralised supply: a social cost–benefit analysis in the residential and service
sectors, Energy Policy 34 (2006) 804–832
[7]. K. Balamurugana, Dipti Srinivasana, Thomas Reindlb, Impact of Distributed Generation on Power Distribution Systems, PV Asia
Pacific Conference 2011, Energy Procedia 25 ( 2012 ) 93 – 100
[8]. M Damodar Reddy and V. C. Veera Reddy, Capacitor placement using fuzzy and particle swarm optimization method for
maximum annual Savings, ARPN Journal of Engineering and Applied Sciences, vol. 3, No.3, June 2008
[9]. M. Damodar reddy and N.V. Vijay kumar, Optimal capacitor placement for loss reduction in distribution system using fuzzy and
harmony search algorithm, ARPN journal of Engineering and applied sciences, vol.7, No.1, January 2012, ISSN 1819-6608.
[10]. K. Dhananjaya Babu, M. Damodar Reddy, Optimal placement of SVC using Fuzzy and PSO algorithm, International Journal of
Engineering Research and Applications (IJERA) ISSN: 2248-9622, Vol. 3, Issue 1, January -February 2013, pp.485-490
[11]. Esmat Rashedi, Hossein Nezamabadi-pour, Saeid Saryazdi, GSA: A Gravitational Search Algorithm Information Sciences 179
(2009) 2232–2248
[12]. G W Stagg, Ahmed H El Abiad, Computer Methods in Power System Analysis, McGraw Hill publication
[13]. http://www.ee.washington.edu/research/pstca/
[14]. Tamilnadu Electricity Board Statistics at a Glance - 1999-2000, compiled by Planning Wing of Tamilnadu Electricity Board,
Chennai, India
Author’s Details:
Mr. K. Dhananjaya Babu is a Full – Time research scholar pursuing his Ph. D in Department of
Electrical and Electronics Engineering, Sri Venkateswara University College of Engineering,
Tirupati since 2013. He received his M. tech degree from the same university in 2012. He
received his B. tech degree from JNT University, Hyderabad in 2007. He has two years industrial
experience as operation engineer.
Mrs. A. Lakshmi Devi is a senior professor in Department of Electrical and Electronics
Engineering, Sri Venkateswara University College of Engineering, Tirupati. She received her
Doctoral Degree from the same University in 2008. She received her Master’s Degree from IISc
Bangalore in 1993. She received her B. tech from Sri Venkateswara University College of
Engineering in 1991.](https://image.slidesharecdn.com/e010123337-151119085508-lva1-app6892/85/Application-of-Gravitational-Search-Algorithm-and-Fuzzy-For-Loss-Reduction-of-Networked-System-Using-Distributed-Generation-5-320.jpg)
Application of Gravitational Search Algorithm and Fuzzy For Loss Reduction of Networked System Using Distributed Generation
- 1. IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE) e-ISSN: 2278-1676,p-ISSN: 2320-3331, Volume 10, Issue 1 Ver. II (Jan – Feb. 2015), PP 33-37 www.iosrjournals.org DOI: 10.9790/1676-10123337 www.iosrjournals.org 33 | Page Application of Gravitational Search Algorithm and Fuzzy For Loss Reduction of Networked System Using Distributed Generation K. Dhananjaya Babu1 , Dr. A. Lakshmi Devi2 1 (Research Scholar, Department of EEE, SVU College of Engineering, SV University, Tirupati, India) 2 (Professor, Department of EEE, SVU College of Engineering, SV University, Tirupati, India) Abstract: The insertion of distributed generator (DG) into distribution systems for performance improvement has been an obvious application. But the choice of DG has been extended to the networked systems as well in the recent years. The penetration level of DG is accelerating due to the liberalization of electricity markets and Technological advances in small generators and energy storage devices. In this paper the DG technology is used for loss reduction in the networked system. Functioning of DG is effective not merely on DG itself, but through proper location and size of the same. In this paper optimal magnitude of power from DG is computed by using a new algorithm (i.e. Gravitational Search Algorithm (GSA)), upon finding optimal locations through fuzzy inference system. The proposed combination of GSA and Fuzzy concept is tested on an IEEE 5 bus system, IEEE 14 bus system and 62-bus practical Indian system. Keywords: Distribution Generation, Fuzzy Approach, Gravitational Search Algorithm I. Introduction Distributed Generation(DG) is utilised a power supply in distribution systems which was extended to power transmission system for - choking transmission loss, improving voltage profile, and reducing the congestion etc. The integration of DG with the utility system at permissible voltage involves usage of small scale distributed energy resources. DG mainly constitutes non-conventional and renewable energy sources like solar PV, wind turbines, etc. [1] DG involves the interconnection of small-scale distributed energy resources with the main power utility at distribution voltage level [2-4]. DG can reduce power loss and can improve node voltages. Further power loss lowering can be at expense of worse voltage profile and vice versa [5]. These two main outcomes should be compromised to get an optimal overall performance. Here these effects are highly dependent on DG allocation in the transmission or distribution system. So the sizing and locations of DG have to be computed carefully to optimize the overall Performance, which will result in technical and economic benefits [6-7]. In this paper section II describes the Fuzzy approach for DG optimal locations which rely on loss and voltage indices. Section III describes the methodology for DG sizing using GSA. Section IV presents the result analysis and discussion for the proposed concept. II. Fuzzy Approach For Optimal DG Locations Fuzzy logic is a technique that allows for quantification and processing of common language rules to arrive at a decision. Al1 the rules are considered at once or in parallel to arrive at a weighted decision [8-9]. In this paper the basics of applying the fuzzy logic method is for finding optimal locations of DG. Newton-raphson method is used for load flow study, which was applied on IEEE 5 bus system, IEEE 14 bus system, and 62 bus Indian practical systems. 2.1 Loss index In this index the real power loss reduction is observed when total load is completely or partially removed at a particular load bus. The loss index is determined using (1). LI = P1 - P2 (1) Where, LI = Loss Index P1 = Power loss for normal load P2 = Power loss for partial load The above obtained loss reductions are then, linearly normalized into a [0, 1] range with the largest loss reduction having a value of 1 and the smallest one having a value of 0. The normalised loss index is given as one of the two indexes to the fuzzy inference system.
- 2. Application of Gravitational Search Algorithm and Fuzzy for Loss Reduction … DOI: 10.9790/1676-10123337 www.iosrjournals.org 34 | Page Figure 1: One line diagram of Indian 62-bus practical system 2.2 Voltage Index This index is taken as a second input to the fuzzy inference system, here the voltages are also normalized into a [0, 1] range with the largest voltage having a value of 1 and the smallest one having a value of 0. Figure 2: Block diagram of Fuzzy inference system Rule base and the membership function for the inference system shown in Fig. 2 were adopted from [10]. The DG location index obtained from the inference system will be considered as optimal locations for DG in priority wise i.e. index nearing 1 will be best, and for 0 it will be worst case. III. Methodology For Calculating Sizing Of DG Gravitational Search Algorithm (GSA) is proposed by Esmat Rashedi et al in 2009[11], which is rely on law of gravity and mass interactions. In this algorithm, the searcher agents are a collection of masses which interact with each other based on the Newtonian gravity and the laws of motion. In GSA, each position of mass (agent) has a solution, wherein the position of heaviest mass has optimum solution which attracts other masses, by proper acceleration of their gravitational and inertia masses. At the end iteration count the heaviest mass will present an optimum solution in the search space. In this study GSA is used for optimizing the capacity of DG by randomly generating the masses between prescribed limits. Algorithm for DG sizing using GSA Step 1: Initially [nom x n] number of masses are generated randomly within the limits, where ‘nom’ is the population size and ‘n’ is the number of DG units. Each row represents one possible solution to the optimal DG- sizing problem. Step 2: Similarly [nom x n] number of initial velocities is generated randomly between the limits. Iteration count (T) is set to one. Step 3: Calculate the force on the ith mass by the remaining other masses using (2) Fij = G× Mp× Ma Rij+ ε × DGxj d - DGxi d (2)
- 3. Application of Gravitational Search Algorithm and Fuzzy for Loss Reduction … DOI: 10.9790/1676-10123337 www.iosrjournals.org 35 | Page Where, Maj = Active gravitational mass related to agent j, Mpi = Passive gravitational mass related to agent i, G = Gravitational constant, ε = Small constant, and Rij = Euclidian distance between two agents i and j: Rij = DGxi,DGxj 2 (3) The total force that acts on ith mass in a dimension d be a randomly weighted sum of dth components of the forces exerted from other agents. Fi d = randi. Fij dN i=1, j ≠ i (4) Where, rand = random number generated between 0 and 1. Step 4: After finding the net force on each mass due to the other masses, the acceleration for each mass is calculated using (5) ai d = Fi d Mi (5) Step 5: The velocities and position of all the masses are updated using (6), (7) Vi d = randi × Vi d + ai d (6) DGxi d = DGxi d + Vi d (7) Step 6: Fitness values of all the masses are calculated Step 7: Set error tolerance (e) = 0.0001, if ‘e’ is greater than the difference of best of fitness matrix and mean of fitness matrix, then go to Step 8, else go to Step 9. Step 8: The current iteration (T) count is incremented and if ‘T’ is not reached maximum then go to step 3 Step 9: The heaviest mass in the population gives the best fitness value i.e. maximum loss reduction and position of that mass gives the optimal DG sizes. IV. Result And Discussion The proposed concept which is a two – stage methodology has applied on standard IEEE systems as well on Indian practical system. IEEE 5 bus system [12] contains 1 slack/generator buses (bus numbers: 1,), 4 load buses (bus numbers: 2, 3, 4, and 5) and 7 transmission lines. Fuzzy approach gives 3rd location as optimal choice, where GSA optimized the sizing of DG with which the losses in the system has reduced significantly and voltage profile has increased significantly. Figure 3: Voltage profile before and after DG placement for IEEE 5 bus system
- 4. Application of Gravitational Search Algorithm and Fuzzy for Loss Reduction … DOI: 10.9790/1676-10123337 www.iosrjournals.org 36 | Page Figure 4: Voltage profile before and after DG placement for IEEE 5 bus system Figure 5: Voltage profile before and after DG placement for Indian 62 bus practical system Table 1: Total Loss before and After Placement of DG using GSA Test System Used Optimal Location by Fuzzy Approach Total Loss Before DG Placement (MW) Iterations generated Optimized Capacity of DG (MW) Total Loss After DG Placement (MW) IEEE 5 bus system 3 4.5868 32 108.0316 1.3231 IEEE 14 bus system 5 13.3934 33 178.8503 5.2879 Indian 62 bus Practical system 11, 30 64.0512 30 101.7489, 76.9365 60.2518 IEEE 14 bus system [13] contains 5 generator buses (bus numbers: 1,2,3,6 and 8), 9 load buses (bus numbers: 4, 5, 7,9,10,11,12,13 and14) and 20 transmission lines. Fuzzy approach gives 5th location as optimal choice, where GSA optimized the sizing of DG with which the losses in the system has reduced significantly and voltage profile has increased considerably. Indian 62 bus practical system [14] contains 19 generator buses (bus numbers: 1, 2, 5, 9, 14, 17, 23, 26, 32, 33, 34, 37, 49, 50, 51, 52, 54, 57, and 58), the remaining are load buses and 89 transmission lines. Fuzzy approach gives 11th and 30th location as optimal choice, where GSA optimized the sizing of DG in both the locations with which the losses in the system has reduced considerably and voltage profile has increased a little on some buses. V. Conclusion The proposed concept has been successfully applied on IEEE 5 bus, IEEE 14 bus and Indian practical system. The Fuzzy approach has given best locations (based on index calculated), for installation of DG. For sizing of DG the Gravitational Search Algorithm optimized accurately for best loss reduction and better voltage profile. When compared the results obtained for IEEE standard systems and Indian practical systems, the effective ness of DG on both the systems is not same, because the real systems are complicated having more uneven transmission lines with different loading conditions. To some extent, application of DG is a good option for loss reduction in real system.
- 5. Application of Gravitational Search Algorithm and Fuzzy for Loss Reduction … DOI: 10.9790/1676-10123337 www.iosrjournals.org 37 | Page Every optimization technique when verified on standard test functions will converge perfectly irrespective of time consumed. A optimization is said to be a better one based on the computation time required, here GSA is a best technique which take less time, because the masses are updated by taking the cumulative difference of all masses available, whereas in standard PSO, Firefly Algorithm etc. each particle is been updated with reference to the global best practical. References [1]. Thomas Ackermann , Goran Andersson , Lennart Soder ,Distributed generation: a definition, Electric Power Systems Research 57 (2001) 195–204 [2]. Gopiya Naik S, D. K. Khatod and M. P. Sharma, Optimal Allocation of Distributed Generation in Distribution System for Loss Reduction, 2012 IACSIT Coimbatore Conferences IPCSIT vol. 28 (2012) © (2012) IACSIT Press, Singapore [3]. Thomas E Hoff, Howard J Wenger, and Brian K Farmer, Distributed generation An alternative to electric utility investments in system capacity, Energy Policy. Vol. 24. No. 2. pp. 137-147. 1996 [4]. Naresh Acharya, Pukar Mahat, N. Mithulananthan An analytical approach for DG allocation in primary distribution network, Electrical Power and Energy Systems 28 (2006) 669–678 [5]. Lucian Ioan Dulau, Mihail Abrudean, Dorin Bica, Effects of distributed generation on electric power systems, The 7th international conference interdisciplinary in engineering 2003, Procedia technology [6]. Francesco Gullı´, Small distributed generation versus centralised supply: a social cost–benefit analysis in the residential and service sectors, Energy Policy 34 (2006) 804–832 [7]. K. Balamurugana, Dipti Srinivasana, Thomas Reindlb, Impact of Distributed Generation on Power Distribution Systems, PV Asia Pacific Conference 2011, Energy Procedia 25 ( 2012 ) 93 – 100 [8]. M Damodar Reddy and V. C. Veera Reddy, Capacitor placement using fuzzy and particle swarm optimization method for maximum annual Savings, ARPN Journal of Engineering and Applied Sciences, vol. 3, No.3, June 2008 [9]. M. Damodar reddy and N.V. Vijay kumar, Optimal capacitor placement for loss reduction in distribution system using fuzzy and harmony search algorithm, ARPN journal of Engineering and applied sciences, vol.7, No.1, January 2012, ISSN 1819-6608. [10]. K. Dhananjaya Babu, M. Damodar Reddy, Optimal placement of SVC using Fuzzy and PSO algorithm, International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622, Vol. 3, Issue 1, January -February 2013, pp.485-490 [11]. Esmat Rashedi, Hossein Nezamabadi-pour, Saeid Saryazdi, GSA: A Gravitational Search Algorithm Information Sciences 179 (2009) 2232–2248 [12]. G W Stagg, Ahmed H El Abiad, Computer Methods in Power System Analysis, McGraw Hill publication [13]. http://www.ee.washington.edu/research/pstca/ [14]. Tamilnadu Electricity Board Statistics at a Glance - 1999-2000, compiled by Planning Wing of Tamilnadu Electricity Board, Chennai, India Author’s Details: Mr. K. Dhananjaya Babu is a Full – Time research scholar pursuing his Ph. D in Department of Electrical and Electronics Engineering, Sri Venkateswara University College of Engineering, Tirupati since 2013. He received his M. tech degree from the same university in 2012. He received his B. tech degree from JNT University, Hyderabad in 2007. He has two years industrial experience as operation engineer. Mrs. A. Lakshmi Devi is a senior professor in Department of Electrical and Electronics Engineering, Sri Venkateswara University College of Engineering, Tirupati. She received her Doctoral Degree from the same University in 2008. She received her Master’s Degree from IISc Bangalore in 1993. She received her B. tech from Sri Venkateswara University College of Engineering in 1991.