This document presents a method for optimizing the placement and sizing of multiple distributed generation (DG) units in a transmission system to minimize power losses and improve voltage. Fuzzy logic is used to determine the optimal locations for DG units based on power loss index and voltage. Particle swarm optimization is then used to determine the optimal size of DG units at the identified locations. The method is tested on the IEEE 14-bus system, showing that placing DG units at buses 3, 4 and 5 can reduce power losses by up to 94.47% and improve voltages compared to using a single DG unit.

1. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 5, Issue 4, April (2014), pp. 20-26 © IAEME
20
LOSS MINIMIZATION AND VOLTAGE IMPROVEMENT IN
TRANSMISSION SYSTEMS USING MULTI DISTRIBUTED GENERATION
P. Sobha Rani1
, A. Lakshmi Devi2
, K. Dhananjayababu3
1
Assoc. Professor, Dept. E.E.E., N.B.K.R.I.S.T., Vidyanagar
2
Professor, Dept.E.E.E., S,V.U. College of engineering, Tirupati
3
Research scholar, Dept.E.E.E., S.V.U.College of engineering, Tirupathi
ABSTRACT
Recent changes in the electric utility infrastructure have created opportunities for much
technological innovation like employment of distributed generation (DG). DG devices can be
strategically placed in power systems for grid reinforcement, reducing power losses and on-peak
operating costs. It is important to define the size and location of local generation unit that is to be
placed in the given transmission network. Optimal sizing and placement of DG unit reduces the
power losses. In this paper for optimal location fuzzy approach is used. Particle swarm optimization
(PSO) is used for optimal sizing of DG unit. The proposed method is tested on IEEE-14 bus system.
Keywords: Distributed Generation (DG), Fuzzy Logic, Particle Swarm Optimization (PSO),
Power Loss, Voltage.
I. INTRODUCTION
In the transmission line resistance, shunt conductance, inductance and shunt capacitance are
distributed along the entire length of line. The overhead transmission lines are classified as: short,
medium and long lines. There may be buses with only generators and no loads and others with only
loads and no generators. Reactive power generators may also be connected to some buses.
Distributed generation is defined as small scale generation, located at or near the load centers. DG
devices can be strategically placed in power systems for grid reinforcement, reducing power losses,
and on-peak operating costs, improving voltage profiles and improving system integrity, reliability
and efficiency. Distributed generation has both positive and negative impact on power quality. This
will depend on the type and amount of DG, as well as power interfaces and control schemes used to
connect these units to grid. The development of DGs will bring new changes to traditional power
systems. The beneficial effects of DG mainly depend on its location and size. Selection of optimal
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ISSN 0976 – 6545(Print)
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2. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976
ISSN 0976 – 6553(Online) Volume 5, Issue 4, April (2014), pp.
location and size of DG is a necessary process to maintain the stability and reliabili
system effectively before it is connected to power grid.
II. PROBLEM FORMULATION
In a large transmission system network with high power losses
particular bus from many buses so as to place a DG unit for loss reduction. Power losses are present
at every bus and identification of bus with highest power loss is important because losses at that bus
includes majority of total losses in the sy
minimizing power losses. This can be achieved by DG unit placement in the network. If DG size
exceeds certain value of limit, power loss at that bus becomes negative. This situation must be
avoided. In this paper the main focus is on voltage improvement and reduction of power loss for a
transmission system using DG units.
In this paper Newton Raphson method is used for load flow study.
fuzzy logic approach for optimal placement of DG and Particle swarm optimization method for
optimal size of DG.
FUZZY LOGIC IMPLEMENTATION
In this paper for location of DG on load buses
developed with two objectives: reducing real power losses and maintaining voltage profile within the
allowable limits (0.9p.u-1.1p.u). For writing fuzzy rules two inputs
nodal voltages (p.u) are taken.
LRi = Pi
1
–Pi
2
for i
Where LR - loss reduction
Pi
1
- real power for normal load flow
Pi
2
- real power for load flow by total compensation of
The loss reduction input is normalized using equation(2) so that values fall between 0 and 1,
where the largest number having a value of 1 and
PLI=
The output of fuzzy gives the suitability index for DG placement. Maximum values are the
promising locations for DG placement.
Fig 1: Member
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976
6553(Online) Volume 5, Issue 4, April (2014), pp. 20-26 © IAEME
21
location and size of DG is a necessary process to maintain the stability and reliabili
before it is connected to power grid.
a large transmission system network with high power losses, it is difficult to
particular bus from many buses so as to place a DG unit for loss reduction. Power losses are present
at every bus and identification of bus with highest power loss is important because losses at that bus
includes majority of total losses in the system. The cost of power transmission is reduced by
minimizing power losses. This can be achieved by DG unit placement in the network. If DG size
exceeds certain value of limit, power loss at that bus becomes negative. This situation must be
is paper the main focus is on voltage improvement and reduction of power loss for a
transmission system using DG units.
Raphson method is used for load flow study. This paper presents a
ptimal placement of DG and Particle swarm optimization method for
IMPLEMENTATION
on of DG on load buses, fuzzy approach is used. Fuzzy logic is
developed with two objectives: reducing real power losses and maintaining voltage profile within the
1.1p.u). For writing fuzzy rules two inputs- power loss
for i= 1 to number of load buses ………
loss reduction
real power for normal load flow
real power for load flow by total compensation of DG at ith
node
The loss reduction input is normalized using equation(2) so that values fall between 0 and 1,
where the largest number having a value of 1 and the smallest as 0.
for i= 1 to no. of load buses ……. (2)
The output of fuzzy gives the suitability index for DG placement. Maximum values are the
promising locations for DG placement.
Fig 1: Membership function used for power loss index
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
location and size of DG is a necessary process to maintain the stability and reliability of existing
, it is difficult to select a
particular bus from many buses so as to place a DG unit for loss reduction. Power losses are present
at every bus and identification of bus with highest power loss is important because losses at that bus
stem. The cost of power transmission is reduced by
minimizing power losses. This can be achieved by DG unit placement in the network. If DG size
exceeds certain value of limit, power loss at that bus becomes negative. This situation must be
is paper the main focus is on voltage improvement and reduction of power loss for a
This paper presents a
ptimal placement of DG and Particle swarm optimization method for
approach is used. Fuzzy logic is
developed with two objectives: reducing real power losses and maintaining voltage profile within the
power loss index (PLI) and
………(1)
node
The loss reduction input is normalized using equation(2) so that values fall between 0 and 1,
……. (2)
The output of fuzzy gives the suitability index for DG placement. Maximum values are the

3. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 5, Issue 4, April (2014), pp. 20-26 © IAEME
22
Fig 2: Membership function for node voltages
Fig 3: Membership function used for fuzzy output
Particle swarm optimization for optimal sizing
Particle swarm optimization is a computational method that optimizes a nonlinear and multi-
dimensional problem. Population of birds or fish is known as swarm. Each candidate of swarm is
known as particle. These particles move around in the search space searching for their goal, the place
which best suits their needs given by a fitness function. Once a problem space is defined a set of
particles spawned in it and their positions and velocities are updated iteratively according to specific
PSO algorithm. Every particle using eq (3) and (4) modifies its position to reach the global best
position.
Vi
k+1
=kc [W Vi
k
+C1 randi (pbesti –Xi) + C2 randi (gbesti – Xi)] …… (3)
Xi
k+1
= Xi
k
+ Vi
k+1
.….. (4)
Where kc =constriction factor
Vi
k
=velocity of a particle I in kth
iteration
W = inertia weight parameter
C1 , C2 = weight factors
rand1 , rand2 =random numbers between 0 and 1
Xi
k
- position of particle in kth
iteration
Inertia weight is calculated using eq (5) for better exploration of search space.

4. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 5, Issue 4, April (2014), pp. 20-26 © IAEME
23
W= Wmax–
ሺௐ௠௔௫ିௐ௠௜௡ሻ‫כ‬௧
்
….. (5)
where Wmax , Wmin are the constraints for inertia weight factor
t = current iteration count
T = maximum number of iterations
The constraints are
Vi
min
≤ Vi ≤ Vi
max
……. (6)
Xi
min
≤ Xi ≤ Xi
max
…….. (7)
Algorithm
1. Initialize (p*n) number of particles, where p is population size and n is number of DG
devices.
2. (p*n) number of initial velocities are generated randomly between the limits.
3. Iteration count is set to one.
4. By placing all ‘n’ DG devices of each particle at respective candidate location, load flow
analysis is performed.
5. Fitness value corresponding to each particle is evaluated:
Fitness= PL –PL
DG
for maximum loss reduction.
Initially all fitness is copied to pbest fitness. Maximum of pbest fitness gives gbest fitness
which is a measure for maximum loss reduction, and the corresponding particle represents
gbest particles.
6. New velocities for all the particles within the limits are calculated using eq (3). Particle
positions are updated using eq (4).
7. Once the particles are updated, load flow analysis is performed, new fitness is calculated
using eq (6) If new fitness is greater than pbest fitness then corresponding particle is moved
to pbest particle.
8. Maximum of pbest fitness gives gbest fitness and corresponding particle is stored as gbest
particle.
9. From pbest fitness, maximum fitness and average fitness values are calculated. Error is
calculated using eq (8)
Error = maximum fitness – average fitness …… (8)
If this error is less than a specified tolerance, go to step 9.
10. Increment the current iteration count. If iteration count is not reached maximum, go to step 6.
11. Gbest fitness gives maximum loss reduction and gbest particle gives optimal DG size.
III. SIMULATION
The proposed method is tested on IEEE-14 bus system. Three DGs are considered. Three
DGs will improve the power loss by 94.47% as compared to 70.41% for single DG. The results are
shown in the following table.

5. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 5, Issue 4, April (2014), pp. 20-26 © IAEME
24
Table 1: Summary of results for multi DG
No.of
DG
Bus No. Power loss (p.u) DG size Voltage (p.u)
Without
DG
With
DG
%loss
reduction
Without
DG
With
DG
% voltage
improvement
1 DG 3 13.393 3.9635 70.41 141.77 1.01 1.04 2.97
2 DG 3 13.393 0.8673 93.52 96.7175 1.01 1.04 2.97
4 120.516 1.0183 1.0425 2.37
3 DG 3 13.393 0.7437 94.47 97.999 1.01 1.04 2.97
4 89.8149 1.0183 1.042 2.32
5 39.5512 1.02 1.038 1.85
Fig 4: Total real losses in 14 bus system before and after placement of DG
Fig 5: Comparison of line losses before and after placement of DG

6. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 5, Issue 4, April (2014), pp. 20-26 © IAEME
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Fig 6: Bus voltages before after placement of single DG
Fig 7: Bus voltages before and after placement of two DGs
0 2 4 6 8 10 12 14
1.01
1.02
1.03
1.04
1.05
1.06
1.07
1.08
1.09
1.1
1.11
number of buses
voltages
before placement of DGs
after placement of DGs
0 2 4 6 8 10 12 14
1.01
1.02
1.03
1.04
1.05
1.06
1.07
1.08
1.09
1.1
1.11
number of buses
voltages
before placement of DGs
after placement of DGs

7. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 5, Issue 4, April (2014), pp. 20-26 © IAEME
26
Fig 8: Bus voltages before and after placement of three DGs
IV. CONCLUSION
In this paper a particle swarm optimization method for optimal placement of multi DG is
considered. There is a significant decrease in real and reactive power losses with multi DG
placements. It can be seen that voltage profile is improved in using DGs.
REFERENCES
1. Griffin T, Tomsovic K, Secrest D and Law A, “Placement of dispersed generations systems for
reduced losses”, proceedings of the 33rd
Annual Hawaii international conference on system
sciences, No.2, pp, 1446-1454, 2000
2. S.Nagendra and A.Lakshmi Devi, “Power loss reduction and voltage improvement in
transmission systems using Distributed generator units: A case study”, The ICFAI university
Journal of Electrical & Electronics Engineering, vol.2, No.2, 2009.
3. A.M.EI-Zonkoly, “Optimal placement of multi distributed generation units including different
load models using pso”, swarm and evolutionary computation, vol 1, pp-50-59, 2011.
4. Rajendra Prasad Payasi, Ashish K.Singh and Devender singh, “Review of DG planning,
objectives, constraints, algorithms”, International journal of engineering science & Technology,
vol.3, 2011.
5. W.EI-Hattam and M.M.A.Salama, “DG technologies, Definitions & benefits”, Electric power
system research, vol.71, pp.119-128, 2002.
6. P. Sobha Rani and Dr. A. Lakshmi Devi, “Performance Improvement of Distribution System
with Multi Distributed Generation using Particle Swarm Optimization”, International Journal of
Electrical Engineering & Technology (IJEET), Volume 5, Issue 2, 2014, pp. 44 - 50,
ISSN Print : 0976-6545, ISSN Online: 0976-6553,
0 2 4 6 8 10 12 14
1.01
1.02
1.03
1.04
1.05
1.06
1.07
1.08
1.09
1.1
1.11
number of buses
voltages
before placement of DGs
after placement of DGs