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Voltage_Stability_Analysis_With DG NEW (1).pptx
1. INTRODUCTION
• One of the modern and important techniques in the electrical
distribution systems is to solve the networks problems service
availability, high loss and to improve system voltage.
• These can be resolved by accommodating small scaled de-
centralized generating stations in networks, which is known as
Distributed Generation (DG).
• Distributed generation (DG) units reduce electric power losses and
hence improve reliability and voltage profile. Determination of
appropriate size and location of DG is important to maximize
overall system efficiency.
• DG units are typically connected so that they work in parallel with
the utility grid, and they are mostly connected in close proximity to
the load.
2. • Distribution systems with embedded DG units can operate in two
modes: grid-connected and autonomous.
• In grid-connected mode, although the voltage and frequency are
typically controlled by the grid and the DG units are synchronized
with the grid.
• Integrating DG units can have an impact on the practices used in
distribution systems, such as the voltage profile, power flow, power
quality, stability, reliability and protection.
• Since DG units have a small capacity compared to central power
plants, the impact is minor if the penetration level is low. However,
if the penetration level of DG units increases the impact of DG units
will be profound. Further more, if the DG units operate in
autonomous mode, as a microgrid, the effects on power stability and
quality are expected to be more dramatic because of the absence of
the grid support
3. LITERATURE SURVEY
• Distributed Generation (DG) is small-scale generation units located near or at
loads. However, the definition can be diversified based on voltage level, unit
connection, type of prime-mover and maximum power rating [1].
• IEEE [2] defines DG as “the generation of electricity by facilities that are
sufficiently smaller than central generating plants so as to allow
interconnection at nearly any point in a power system.
• A good number of research work is going on DG integration with grid and its
safe and reliable operation [3][4].
• In the literature, DG planning has been proposed based on power loss
minimisation,voltage stability improvements, reliability improvements etc.
These planning techniques are followed by different methods, i.e., different
analytical, heuristic approaches, and meta-heuristics based on evolutionary
algorithms.
4. • In the paper [5][6] authors have used the analytical method and rule of thumb
for analysing the distribution system which is radial and has uniformly
distributed loads. Rule is simple and easy to use, according to which the DG
size is 2/3 that of the kVar load and it is located at 2/3 of the distance from a
radial feeder but it cannot provide the proper solution when the load
distribution type is changed. Moreover, it can not be applied in meshed
network.
• In the paper [7], an analytical approach has been presented to identify
appropriate location to place single DG in radial as well as loop systems to
minimize losses. But, in this approach, optimal sizing is not considered.
• For dealing with DG sizing and placement issues, heuristic methods have
been proposed by several researchers [8][9]. To ensure the optimal use of an
existing network’s assets, a methodology based on linear programming (LP)
is proposed.
5. • For selection of optimum size and location of DG, several genetic
algorithms (GA) and fuzzy logic based methods have been discussed in
[10][11][12]], Although GA provides almost near optimum output but they
are computationally very demanding and have a slow convergence .
• As load flow represents the system states, therefore it can be used for
planning the future expansion of power systems. We can calculate the
system loss from the load flow result and doing the load flow repeatedly,
we can easily tell the location and size of DG for which we get the
minimum power loss of the system. This method is known as exhaustive
load flow (ELF) method. Although this ELF method gives the exact
answer; however, it needs lots of load flow computation. Therefore, ELF
method needs to be optimized to get accurate answer and less
computational time.
• Although the integration of the DG units in electric systems has
multifarious benefits, they increase the complexity [13]. Therefore, the DG
units have impacts on the system performance such as voltage profile,
power flow, system losses, power quality, stability, reliability, and
protection [14]. The main target of this research is to study the voltage
stability due to the high penetration of the DG units[15][16].
6. • The stability of a power system is defined as “a property of a power
system that enables it to remain in a state of equilibrium under
normal operating conditions and to regain an acceptable state of
equilibrium after being subjected to a disturbance” [17].
• Therefore, the penetration of DG units into distribution systems
affects the stability of the system, and as the penetration level
increases, stability becomes a significant issue [18]. Any fault
occurring in the distribution system might cause voltage and angle
instability.
• Because of increase in load demand the distribution system is facing
problems. They are experiencing many changes from a low level to
high level of load. M. Chakravorty, D. Das [19] proposed a voltage
stability index technique for radial distribution systems. Voltage
Stability Index (VSI) represents a numerical solution to identify the
sensitive node of the system.
7. • Optimal placement of multi-distributed generation units including
different load models using particle swarm optimization for loss
minimization and voltage profile improvement is explained in [20].
• The comparison of Harmony Search Algorithm and Particle Swarm
Optimization for Distributed Generation Allocation to Improve
Steady State Voltage Stability of Distribution Networks with
different methods is done in [21].
• The Power flow study and voltage stability analysis for radial
system with distributed generation by using different index is
explained in [22][23].
8. STATEMENT OF THE PROBLEM
• Interest in Distributed Generation (DG) in power system
networks has been growing rapidly.
• This increase can be explained by factors such as
environmental concerns, the restructuring of electricity
businesses, and the development of technologies for
small-scale power generation.
• DG units are typically connected so as to work in parallel
with the utility grid; however, with the increased
penetration level of these units and the advancements in
unit’s control techniques, there is a great possibility for
these units to be operated in an autonomous mode known
as a micro grid.
9. • With the development of economy, load demands in
distribution networks are sharply increasing. Hence, the
distribution networks are operating more close to the voltage
instability boundaries .
• The integration of distributed generation in distribution system
introduces possibility of encountering some active/reactive
power mismatches resulting in some stability concerns at the
distribution level .
• The inappropriate size and allocation of DG can cause low or
over voltage in the distribution system leading to voltage
instability.
10. • Therefore, another goal of our analysis is to check whether the
voltage profile remains within permissible limit. So, voltage
constraint becomes,
Vmin ≤ V ≤ Vmax
• During this analysis, as per the standard we considered 6%
variable voltage as acceptable stable voltage limit.
i.e. Vmin= 0.94 p.u and Vmax=1.06 p.u.
11. OBJECTIVES OF PROPOSED RESEARCH WORK
Motivated by the above problems, the ultimate goal of this research
is to enhance the voltage stability of distribution systems with DG
integration. Moreover, this research is targeting the following
objectives:
• To make use of real and reactive power flow and power loss
sensitivity factors to find optimal position and size of DG in
reducing power loss and improve voltage stability in distribution
system.
• Analyze the impacts of the DG units on voltage stability in
distribution system for different loading condition with and without
DG.
• .To investigate the effect of DG penetration on system power losses
and voltage stability using the different types of DGs.
• To calculate a multi-objective function to minimize the real
power loss (PL), reactive power loss (QL) and voltage
Deviation (VD).
12. • To calculate the reliability index based on voltage deviation in
the system with and without DG.
• To design a hybrid approach for optimizing DG location and
size in a distribution system. The hybrid algorithm will
combine both BPSO and WOA optimization technique
features.
• To test the hybrid algorithm 33 and 69 –bus system is
considered and compare the results with those obtained by
other researchers using different optimization methods.
• Test the proposed methods by considering practical
distribution system.
13. FORMULA TO FIND SENSITIVITY
• For any distribution system, if DG size is varied from
PDG1 to PDG2 and their corresponding change in power
loss is respectively PL1 to PL2, then the sensitivity factor
becomes,
• In our analysis, Sensitivity factors are evaluated for each
bus using equation and the bus with maximum sensitivity
is identified. Only those buses which have sensitivity
factors close to the maximum value have been considered
in our analysis. Thus solution space is reduced to only a
few buses.
14. • After that, for each of these buses, power loss has been
determined using large step size of DG variation and then
graph is drawn using these few samples.
• The minimum value of the curve that represents the minimum
loss gives the optimum size for that bus and corresponding
generation is the optimum DG size. The bus which is
responsible for minimum loss of the system is the appropriate
location for DG allocation.
15. SIMULATION RESULTS AND DISCUSSION
The proposed method has been applied to a standard
13-bus and 33-bus system which have been taken as
the bench mark problem in many IEEE papers.
16. STEPS TO CARRY OUT SIMULATION USING POWER WORLD SIMULATOR
• Draw the buses and enter the data.
• Draw the transmission lines and enter the data as given in the test system.
• Draw the generators and enter the data.
• Draw the load and enter the data.
• Now run the model and observe the voltage at all the buses and total losses in the
system without DG.
• Calculate sensitivity of each bus with small penetration of DG
• Make list of most sensitive buses
• Select a bus from the list and calculate power loss for large variation of DG size
• Continue until power loss starts to increase and record each sample
• Check whether all sensitive buses have been analyzed
• Find the bus which has minimum power loss
• Find corresponding DG size
• Find the voltages at all the buses with optimum DG size and location
• Check for voltage stability of the system
• If the voltage stability is not maintained in all the buses then increase the DG size at
an optimum location until the voltage stability is maintained at all the buses.
17. The IEEE 13-bus Test system is modelled using power world simulator
as shown in figure
Total Load=1.91 MW and 1.08MVAR
18. The load flow analysis is carried out using power world simulator
for the 13-bus system without distributed generation and results
are tabulated in Table
Slack bus
MW
generation
Slack bus
MVAR
generation
Slack bus
MVA
Generation
Total MW
loss
Total MVAR
Loss
2.0655 1.1253 2.3521 0.1507 0.0459
19. The proposed method is applied to the standard 13-bus system. The sensitivity
factors at all the buses are determined by incorporating 10% of distribution
generation at all the buses other than slack bus. The results are shown in the Table
Bus
number
MW loss
without DG
MW loss
for 10% of DG
Sensitivity
factor
2 0.1507 0.1310 0.1060
3 0.1507 0.1279 0.1108
4 0.1507 0.1266 0.1171
5 0.1507 0.1308 0.0978
6 0.1507 0.1305 0.0983
7 0.1507 0.1163 0.0344
8 0.1507 0.1121 0.1869
9 0.1507 0.1115 0.1893
10 0.1507 0.1132 0.1815
11 0.1507 0.1175 0.1607
12 0.1507 0.1120 0.1874
13 0.1507 0.1102 0.1961
20. By applying the proposed method minimum MW loss i, e. 0.0371
is occurred when distributed generation is incorporated at bus 12
with 60% of generation and it is shown in figure
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0 10 20 30 40 50 60 70 80 90
MW
Losses
% DG at Bus 12
21. Voltage Profile of 13-bus system before and after placement of DG
0.86
0.88
0.9
0.92
0.94
0.96
0.98
1
1.02
Voltage
in
p.u
Bus Numbers
Voltage profile
Voltage without DG
Voltage with DG
22. IEEE 33 - BUS TEST SYSTEM
Total Load:3.72 MW and 2.3 MVAR
• Proposed method is applied to 33-bus system using power
world simulator
23. The NR load flow analysis is carried out using power world
simulator for the existing system without distributed generation
and results are tabulated in Table
Slack bus
MW
generation
Slack bus MVAR
generation
Slack bus
MVA
Generation
Total
MW loss
Total
MVAR
Loss
4.0501 2.5258 4.7731 0.3355 0.2294
24. By analyzing the above tables it can be seen that the minimum
MW loss is occurred when distributed generation is incorporated
at bus 13 with 30 % of generation and it is shown in figure
26. Comparison of results obtained from both software tools is
shown in Table
S. N.
Software
tool Used
Case Study DG Location
DG Size for
Voltage Stability
improvement
1
Power World
Simulator
IEEE 13 - Bus
System
Bus 12 60%
IEEE 33 - Bus
System
Bus 13 50%
2
Mipower
Software
IEEE 13 - Bus
System
Bus 12 60%
IEEE 33 -Bus
System
Bus 13 53%
27. EFFECTS OF LOAD CHANGE ON
LOSSES AND VOLTAGE STABILITY
• In power system load is not constant but it continuously varies,
so it required to study the effects of load change on losses and
voltage stability of the distribution system.
• In this work the effects of load change is carried out in IEEE
13-bus and 33- bus standard system with and without DG.
• The losses and voltage stability is analyzed with and without
DG for 75, 85, 95,105,115 and 125 percentage of load in 13-
bus test system.
• The optimal location of DG is bus 12 and size of DG is 60 %
which is obtained from proposed sensitivity factor method.
31. DG size for different load level in 13-Bus system
32. Real and Reactive power losses in IEEE-13 Bus system
for different loading conditions
Load Level in % 75 85 95 105 115 125
Active Power supplied by
DG in MW
0.9104 1.0342 1.1664 1.3021 1.4392 1.5790
Reactive Power supplied by DG
in MVAr
0.5182 0.5930 0.6678 0.7584 0.8233 0.9034
Active Power Loss in MW
without DG
0.0804 0.0951 0.1239 0.1584 0.1953 0.2367
Active Power Loss in MW with
DG
0.0237 0.0223 0.0219 0.0211 0.0202 0.0170
Reactive Power Loss in MVAr
without DG
0.0312 0.0449 0.0585 0.0985 0.0957 0.1182
Reactive Power Loss in MVAr
with DG
0.0227 0.0278 0.0321 0.0604 0.0457 0.0540
P Loss reduction
in %
70.52 76.55 82.32 86.67 89.66 92.81
Q Loss reduction
in %
27.24 38.08 45.12 38.68 52.25 54.31
36. DG size for different load level in 33-Bus system
37. Real and Reactive power losses in IEEE-33 Bus system for different
loading conditions
Load Level in % 75 85 95 105 115 125
Active Power supplied by
DG in MW 1.485 1.705 1.905 2.145 2.355 2.615
Reactive Power supplied by
DG in MVAr 0.925 1.065 1.185 1.340 1.470 1.635
Active Power Loss in MW
without DG 0.180 0.248 0.276 0.384 0.432 0.580
Active Power Loss in MW
with DG
0.070 0.108 0.156 0.234 0.302 0.310
Reactive Power Loss in MVAr
without DG 0.125 0.175 0.185 0.265 0.295 0.395
Reactive Power Loss in MVAr
with DG 0.035 0.045 0.065 0.185 0.225 0.235
P Loss reduction
in %
61.11 56.45 43.47 39.06 30.09 46.55
Q Loss reduction
in %
72.00 74.28 64.86 30.18 23.73 40.50
38. Effects Of Integration of DG on Reliability of the System
Case VRI for 13-Bus system VRI for 33-Bus system
Without DG 0.4615 0.666
With DG 1.0000 1.000
VRI With and without DG for 13 and 33-Bus test systems
Voltage Based Reliability Index =
If the reliability index value is low then number of customers affected due to low
voltage level is more and for better reliability of the system the index value
should be near to 1.
39. Analysis of Power loss and voltage stability with integration
of Multiple DGs
Cases
DG Capacity
(MW/MVAR)
DG
Location
Active
Power
Loss in
MW
Reactive
power
loss in
Mvar
Percentage
Reduction in
losses
DG1 DG2 DG1 DG2 P Loss Q Loss
NO DG - - - - 0.33 0.22 -- --
One DG 2.025 / 1.260 - 13 - 0.20 0.09 39.39 59.09
Two DG 1.215 / 0.756 0.810 / 0.504 13 33 0.066 0.041 80 81.36
Comparison of Real and Reactive power losses in IEEE-33 Bus system
40. Voltage Profile of 33-bus system with Two DGs into the distribution
system
0
0.2
0.4
0.6
0.8
1
1.2
bus1
bus2
bus3
bus4
bus5
bus6
bus7
bus8
bus9
bus10
bus11
bus12
bus13
bus14
bus15
bus16
bus17
bus18
bus19
bus20
bus21
bus22
bus23
bus24
bus25
bus26
bus27
bus28
bus29
bus30
bus31
bus32
bus33
Voltage
in
P.u
Bus Numbers
Without DG
With One DG
With Two DG
41. Types of Distributed Generation (DG).
The DG’s are grouped into four major types based on the real and
reactive power delivering capability [6].
• Type1: DG capable of delivering both active and reactive power.
DG units based on synchronous machines (cogeneration, gas
turbine, etc.) come under this type.
• Type2: This type of DG is capable of delivering only active power
such as photovoltaic, micro turbines, fuel cells, which are
integrated to the main grid with the help of converters/inverters.
• Type3: DG capable of delivering only reactive power. Synchronous
compensators such as gas turbines and capacitor banks are the
example of this type and operate at zero power factors.
• Type4: DG capable of delivering active power but consuming
reactive power. Mainly induction generators, which are used in
wind farms, come under this category. However, doubly fed
induction generator (DFIG) systems may consume or produce
reactive power i.e. operates similar to synchronous generator
42. ANALYSIS OF POWER LOSS AND VOLTAGE STABILITY WITH
INTEGRATION OF TYPE2, TYPE3 AND
TYPE4 DG
• Power loss and voltage stability analysis is done by
placing Type2, type3 and type4 DG in the 33-bus
system.DG is placed at optimal location with optimal
size which is different for different types of DG and is
calculated by using the above sensitivity factor method
as done for type1 DG.
• The table below gives the DG size, location, losses and
percentage reduction in losses in 33-bus system for
different types of DG
43.
44.
45. VOLTAGE STABILITY AND LOSS REDUCTION ANALYSIS
WITH INTEGRATION OF MULTIPLE DGS
• Power loss and voltage stability analysis is
done by placing combination of different
types of DGs in the 33-bus system. The
different case studies are considered with
different combination as given below
46. • Case 1: Combination of type1 and type 2 DG
In this case Type1 DG is placed at bus 13 which is calculated
from sensitivity factor method and Type2 DG is placed at bus 33
which is the weakest bus in the system and voltage stability and loss
reduction is analysed and is tabulated in table 2.
• Case 2: Combination of type1 and type 3 DG
In this case Type1 DG is placed at bus 13 which is calculated from
sensitivity factor method and Type3 DG is placed at bus 33 which is
the weakest bus in the system and voltage stability and loss
reduction is analysed and is tabulated in table 2.
• Case 3: Combination of type1 and type 4 DG
In this case Type1 DG is placed at bus 13 which is calculated from
sensitivity factor method and Type4 DG is placed at bus 33 which is
the weakest bus in the system and voltage stability and loss
reduction is analysed and is tabulated in table 2
49. Integration of Solar Photovoltaic Generation in a Practical
Distribution System for Loss Minimization and Voltage
Stability Improvement
• This work analyse a practical distribution feeder called
“Devikathikoppa feeder” emanating from 110/11 KV Alkola
substation, situated in Shivamogga, Karnataka, India.
• The feeder has the peak load of 1.977 MW and 0.617 MVAR
with 41 distribution transformers (DTC).The load on the
distribution system increases with time due to rise in population.
As load demand increases, there is an increase in the system
losses and decrease in the voltage profile of the system.
• The consumer at the last bus will face the voltage stability
problem i.e. voltage profile is not within the acceptable limits
therefore there is a decrease in performance of distribution
system.
50. • Distribution Generation (DG) placed at the consumer end will
improve the voltage profile at the buses and decrease the total
loss in the system.
• This work analyses the main problems faced by the practical
distribution consumer’s i.e. reduced supply voltage and high
system losses.
• The voltage profile of the existing “Devikathikoppa feeder" as
shown in Fig. During analysis, as per the standard we
considered 6% variable voltage as acceptable stable voltage
limit i.e. Vmin=0.94 p.u and Vmax=1.06 p.u.
• In the subsequent part, we will show how optimum size and
position of DG impacts on voltage level of interconnecting
buses.
53. • Without DG real-power loss of the system is 0.1334 MW,
reactive-power loss is 0.096 MVAR and minimum bus voltage
is 0.8967 p.u at peak load.
• The following Different cases are considered as below
• Case-1: Integration of only DG units.
• Case-2: Integration of only capacitor.
• Case-3: Incorporation of DG and capacitor simultaneously
54. Case 1: Integration of only DG (solar PV module) unit
• The proposed loss sensitivity factor technique is used to find the optimal
placement and sizing of DG. The optimal placement obtained using this
method is at bus 18 and size of DG is 30% of the total generation obtained
without DG from central grid. The optimal DG location obtained from this
method is at bus-18 with DG size of 0.633 MW.
• After DG placement the real-power loss s reduced to 0.0783MW from
0.1334 MW, reactive-power loss is reduced to 0.0589MVAR from
0.096MVAR and lowest voltage is also improved to0.9401 p u from 0.8967
p u.
Case 2: Integration of only capacitor.
The optimal size and location of shunt capacitor unit for 41- bus system is
calculated by proposed technique and it is0.214MVAR at bus-18. After
capacitor placement the real- power loss is reduced to 0.1188MW from
0.1334MW, reactive-power loss is reduced to 0.088MVAR from
0.096MVAR and minimum bus voltage is also improved to 0.9166 p u
from 0.8967 p u.The results are shown in Table .
55. Development of Intelligent Technique
Hybrid BPSO-WOA Algorithm
• The main aim is to reduce system losses and improving the voltage
profile by optimal location and size of DG in the distribution system.
• System power flow and voltage stability index have been used in
order to come up with the candidate buses for DG location. This helps
in reducing the search space for the algorithm and thus making it to
converge faster.
• The output from BPSO, which is transferred to WOA comprises some
sets of optimal solutions with a DG location and the associated DG
size. The BPSO optimized results was used as its set of initial particles
for WOA. This assists in achieving faster convergence. WOA fine tune
the solutions from BPSO Algorithm so as to come up with an optimal
solution.
57. • The BPSO creates the set of initial particles bit strings and constrains
the velocity value in the interval of [0 1]. Hence, the BPSO algorithm is
utilized to improve the reliability of the distribution network by
minimizing the power losses.
• In N-dimensional search space, Xi = [xi1, xi2, … xiN] and Vi = [vi1, vi2, …
viN] are the two vectors associated with each particle i. During their
search, particles trade information with each others in a definite way
to optimize their search experience. There are different variants of
the particle swarm paradigms but the most commonly used one is the
Gbest model where the whole population is considered as
a single neighborhood throughout the optimization process [16].
• During each iteration, the particle with the best solution shares its
position coordinates (Gbest) information with the remainder of the
swarm. Then, each particle updates its coordinates based on its own
best search experience (Pbest) and Gbest according to the following
equations [1]
58.
59. • The updated particles are not in the form of binary numbers; it
is not suitable for solving the problem of optimal DG
placement. In order to convert the particles as binary values,
the following logistic transformation is utilized which is
written in Eq. (4) and (5).
60. Whale optimization algorithm (WOA)
• In recent times a latest optimization algorithm has introduced in 2016
by Mirjalili and Lewis called whale optimization algorithm which is a
meta heuristic algorithm.
• These whales are treated as extremely intelligent animals with
motion. The WOA algorithm is motivated by the unique hunting
nature of humpback whales.
• Generally the humpback whales choose to hunt krills or small fishes
they are very close to sea surface. Humpback whales use bubble net
feeding method for hunting which is special hunting method.
• In this unique method they generate distinct bubbles all along a circle
also called as 9-shaped path by swimming around the prey.
• The following sections explains the mathematical model of WOA
• Encircling prey.
• Bubble net hunting method.
• Search the prey.
61. • Encircling prey:
• The location of the prey is determined and they are encircled. In this phase,
the current best position is assumed as the best candidate solution and the
rest of the search agents try to update their position toward the best search
agents. The process can be expressed by the following equation:
62. Bubble-net hunting method
a.Shrinking encircling mechanism
• This technique is applied by linearly decreasing the value of from 2 to 0 which
is any value between [-a, a]. The Randomly selected value for a vector is in the
range of [-1, 1]. The updated position of A is obtained between initial position
and current best agent position.
b.Spiral updating position
• To represent the helix-shaped movement between humpback whale and prey
the mathematical equation is written and is given by:
63. • Throughout the preys hunting, humpback whale swims around shrinking circle
and all along the spiral shape path concurrently. Therefore only 50% of assumption
is preferred between shrinking encircling or spiral shape during optimization to
update the position of a whale. Furthermore, two equations are used to model
system as given below.
64. • Search for prey (exploration phase)
• In this method, the vector A is used as exploitation to search for prey.
Thus, Vector 𝐴 takes the random values higher or less than 1 and -
1.Therefore it updates the position based on selected search agents in
place of the best search agents to find the optimum point.
• When 𝐴 >1 will highlight the exploration and allows the WOA to execute
global search. This global search can be expressed in mathematical form
as
65.
66. Voltage Stability Index
• Voltage stability analysis could be performed in a power
system by evaluating the derived voltage stability index.
• The values of the voltage stability index would indicate the
distance to voltage collapse for a given loading condition.
• These indices are taken as an reference that will measure the
stability condition and optimal position of DG will be selected
initially in the power system.
• The voltage stability index (SI) used in this work is developed
for distribution line model from the quadratic equation and is
given by[10]
67. • Where=1,2...N
• SI- Voltage stability index
• Vs- Sending end voltage
• Ri- Resistance of the line
• Xi- Reactance of the line
• PLi- Active power load at bus i
• QLi- Reactive power load at bus i
• N- Number of buses
• The node at which the value of the stability index is minimum,
is more sensitive to the voltage collapse.
68. IEEE 33 - BUS TEST SYSTEM
Total Load:3.72 MW and 2.3 MVAR
71. Results of 33 bus system using BPSO-WOA algorithm using
matlab software
Cases Without DG With one DG With Two DG
DG Location ---- 18 18,33
Active power loss kW 202.68 kW 94.01 86.5476
Reactive power loss
in Kvar
135.16 63.45 51.23
DG Size in MW ------ 1.6545 1.7799
0.2791
DG Size in MVAR ------ 1.23 0.90
0.60
Ploss Reduction in % ---- 53.61 57.29
Qloss Reduction in % ----- 53.05 62.09
73. Comparison results of IEEE-33 Bus system with other
methods
Method DG Size MW DG Location Power loss
KW
% Reduction in
power Loss
PSO[14] 2.576 6 103.98 48.69
WOA[16] 1.255 15 108.406 46.51
Proposed
(BPSO-WOA)
1.6545 18 94.01 53.61
75. Active power losses for different cases
0
50
100
150
200
250
P Loss without DG P Loss with one DG P Loss with Two DG
Active
Power
Loss
in
KW
76. Reactive power losses for different cases
0
20
40
60
80
100
120
140
160
Q Loss without DG Q Loss with one DG Q Loss with Two DG
Reactive
power
Loss
in
KVAR
77. Voltage before and after DG for single type-1 DG
0.84
0.86
0.88
0.9
0.92
0.94
0.96
0.98
1
1.02
1.04
bus1
bus2
bus3
bus4
bus5
bus6
bus7
bus8
bus9
bus10
bus11
bus12
bus13
bus14
bus15
bus16
bus17
bus18
bus19
bus20
bus21
bus22
bus23
bus24
bus25
bus26
bus27
bus28
bus29
bus30
bus31
bus32
bus33
Voltage
in
P.U
Bus Numbers
Voltage profile
Without DG
With DG
78.
79.
80. [10] Presented a paper on “Integration of Solar Photovoltaic Generation in a
Practical Distribution System for Loss Minimization and Voltage Stability
Improvement” in the virtual conference organized by the Department of Electrical
& Electronics Engineering, NMAM Institute of Technology, Nitte during 22 and 23
December 2020.
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v”Application of hybrid techniques for optimal position and sizing of distributed
generation units in radial distribution system” International Journal of Electrical
Engineering & Technology (IJEET), Volume 12, Issue 2, February 2021, pp. 90-99;
ISSN Print: 0976-6545 and ISSN Online: 0976-6553
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Distribution Networks – Impact Studies”, PES Winter Meeting, IEEE, Vol.
1, pp.323-328
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in 33 Bus System Using PSO “International Journal of Advanced Research
in Electrical, Electronics and Instrumentation Engineering, Vol. 4, Issue 4,
April 2015,pp- 2278 – 8875.
[15] C. Borges and D. Falcao, “Impact of Distributed Generation Allocation
and Sizing on Reliability, Losses and Voltage Profile,” in Power Tech
Conference Proceedings, 2003 IEEE Bologna, Vol. 2, June 2003, p. 5.
[16] P. Dinakara Prasad Reddy, V. C. Veera Reddy and T. Gowri
Manohar “Whale optimization algorithm for optimal sizing of
renewable resources for loss reduction in distribution systems”
Renewables (2017) 4:3 DOI 10.1186/s40807-017-0040-1.
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Observing Power Losses and Voltage Stability with Network Constraints”,
IET Gen. Trans. Distrib., Vol.7, Issue.8, pp.813-821, 2013.
[18] Milanovic, J.V.; David, T. M (2002), “Stability of Distribution Networks
with Embedded Generators and Induction Motors”, PES Winter Meeting,
IEEE, Vol. 2, pp. 1023 -1028
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Distribution Networks”, International Journal Of Electrical Power and
Energy Systems, Vol.23, (2), pp.129 – 135, 2001.
[20] El-Zonkoly, A.M. “Optimal Placement of Multi-Distributed Generation
Units Including Different Load Models Using Particle Swarm
Optimization”, IET Gener. Transm. Distrib., 5, (7), pp. 760–771, 2011.
[21] H. Piarehzadeh, A. Khanjanzadeh and R. Pejmanfer. “Comparison of
Harmony Search Algorithm and Particle Swarm Optimization for
Distributed Generation Allocation to Improve Steady State Voltage
Stability of Distribution Networks”, Research Journal of Applied Sciences,
Engineering and Technology, Vo.l/Issue: 4(15), pp. 2310-2315, 2012.
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Power Distribution Network Using Optimum Size and Location of
Distributed Generation”.
[23] Mostafa H. Mostafa “Power Flow Study and Voltage Stability Analysis
for Radial System with Distributed Generation” IJCP, Vol.137, No.9,
March 2016.
[24]Yuvaraj Thangaraja,Ravi Kuppan ”Multi-objective Simultaneous
Placement of DG and DSTATCOM Using Novel Lightning Search
Algorithm”, Journal of Applied Research and Technology (2017) 477–491.
[25] Sirine Essallah, Adel Bouallegue and Adel Khedher “ Optimal Sizing and
Placement of DG Units in Radial Distribution System “ International
journal of renewable energy research , Vol.8, No.1, March, 2018 .
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Capacitor for Loss Reduction, Reliability and Voltage Improvement in
Distribution Networks Using BPSO” I.J. Intelligent Systems and
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