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Optimum Placement of Facts Devices on an Interconnected Power
Systems Using Particle Swarm Optimisation Technique
Uduyok, Awajinwon Charles., Akaninyene B. Obot., Kufre M. Udofia
Department of Electrical and Electronics Engineering, Faculty of Engineering, University of Uyo,
Nigeria
DOI: https://doi.org/10.51244/IJRSI.2025.120800189
Received: 10 Aug 2025; Accepted: 16 Aug 2025; Published: 19 September 2025
ABSTRACT
The increasing demand for electrical power has led to significant challenges in maintaining voltage stability
and meeting reactive power requirements in modern power systems, particularly in Nigeria. This study
investigates the optimal placement of STATCOM (Static Synchronous Compensator) devices in the Gombe
132 kV, 12-Bus transmission network using the Particle Swarm Optimization (PSO) technique. The power
network is modelled and simulated in MATLAB/SIMULINK with the Power System Analysis Toolbox (PSAT)
to assess the impact of STATCOM placement on voltage profile enhancement. The results demonstrate the
effectiveness of STATCOM in mitigating voltage drops and enhancing reactive power control across the
network. The study optimizes Bus-12 as the optimal location for STATCOM placement, resulting in improved
voltage levels within the IEEE standard limits of 0.95 ≤ V ≤ 1.05 p.u. The findings highlight the potential of
PSO-based optimization for enhancing power system stability and reducing transmission losses, offering
valuable insights for improving the reliability of the Nigerian power grid.
Keyword- STATCOM, Particle Swarm Optimization (PSO), Voltage Stability, MATLAB/SIMULINK,
Transmission Network
INTRODUCTION
Modern power systems are increasingly stressed due to escalating electricity demands, leading to significant
challenges in maintaining stable voltage profiles and managing reactive power [1], [2]. Nigeria’s national grid
is particularly vulnerable to voltage instability, characterized by frequent power outages and unreliable service.
Voltage sags, swells, and harmonic distortions are pervasive issues that disrupt the smooth operation of the
grid, especially in areas where transmission networks are overburdened [3]. Despite recent investments in
power generation, the transmission infrastructure has not kept pace with rising demand. As a result, the grid
often operates beyond its designed capacity, contributing to power quality issues [4].
To address these challenges, the integration of Flexible Alternating Current Transmission Systems (FACTS)
has emerged as a promising solution. Specifically, Static Synchronous Compensator (STATCOM) devices have
shown considerable potential in enhancing voltage regulation and improving the capacity for power transfer in
transmission systems. STATCOMs use advanced power electronics to control reactive power, stabilizing
voltage levels, and mitigating transmission losses [5]. However, the effectiveness of STATCOMs is highly
dependent on their optimal placement within the grid, as improper location selection can lead to suboptimal
results [6].
Recent studies have demonstrated the utility of particle swarm optimization (PSO) in optimizing the placement
of FACTS devices in power networks. PSO is recognized for efficiently finding optimal solutions in complex,
nonlinear systems by minimizing voltage deviations and improving system stability [7]-[10]. Despite these
advancements, there remains a gap in applying PSO-based optimization techniques to Nigeria’s 330 kV
transmission network, particularly the Gombe 132 kV, 12-Bus power system, which suffers from poor voltage
stability [4], [11].
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This study aims to address this gap by evaluating the effectiveness of STATCOM devices in improving the
voltage profile of the Gombe 132 kV, 12-Bus power network. Using PSO for optimal STATCOM placement,
this research will provide insights into improving voltage regulation, reducing transmission losses, and
enhancing the overall reliability of Nigeria’s power grid.
MATERIAL AND METHODS
Materials
This section outlines the materials and methods used to analyse the effectiveness of STATCOM devices in
improving the voltage profile of the Gombe 132 kV, 12-Bus power network. The study employed simulation
tools and optimization techniques to evaluate the impact of STATCOM placement within the transmission
network.
1) Software and Simulation Tools
The power network analysis and simulations were conducted using the Power System Analysis Toolbox
(PSAT) in the MATLAB/SIMULINK environment. The PSAT command-line version, compatible with GNU
Octave, was employed for network modelling. At the same time, SIMULINK interface was used for a more
visual representation of the system and the placement of FACTS devices.
2) Gombe 132 kV Transmission Network
The study focused on the Gombe 132 kV, 12-Bus power network, part of Nigeria’s North East region. The
single-line diagram of the network, provided by the Jos Electricity Distribution Company (JEDCO), served as
the basis for the simulations. The transmission network’s parameters, such as line impedance, transformer
ratings, and power generation data, were sourced from Transmission Company of Nigeria (TCN). The network
design was carried out within the PSAT MATLAB environment using the graphical user interface (GUI),
which allows for the easy inclusion of transmission lines, Buses, generators, and other essential components.
Table I shows the transmission line parameters in per unit (p.u.) on a 100 MVA base, detailing line voltage,
length, resistance, reactance, and half-line charging susceptance. Table II provides the power network Bus data,
including Bus voltage, average load, and each Bus’s real and reactive power.
Table I Transmission Line Parameters In P.U. On 100mva Base
S/N
Name of Branch
Length
(km)
Rated Voltage
(kV)
Current Limit
(A)
R (p.u)
X (p.u)
B (p.u)
1
Jos-Gombe
265
330
1360
0.0095
0.051
1.010
2
Jos-Bauchi
118
132
400
0.140
0.280
0.057
3
Bauchi-Gombe
146
132
306
0.172
0.344
0.042
4
Gombe-Biu
126
132
400
0.221
0.311
0.059
5
Biu-Damboa
92
132
400
0.162
0.227
0.042
6
Gombe-Ashaka Junction
84
132
400
0.098
0.197
0.041
7
Ashaka/junction-Factory
10
132
306
0.118
0.024
0.005
8
Ashaka -Potiskum
94
132
306
0.110
0.221
0.046
9
Gombe-Savannah
95
132
306
0.166
0.233
0.044
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10
Savanah-Numan
85
132
306
0.149
0.211
0.039
11
Numan-Yola
50
132
306
0.087
0.123
0.023
12
Damboa-Maiduguri
85
132
400
0.149
0.209
0.039
Source: [12]
Table Ii Bus Data Of The Transmission Network
Bus
No.
Bus Name
Bus Voltage
(kV)
Average load on Bus
(MW)
Real Power P
(MW)
Reactive Power Q
(MVAR)
1
Jos
330
98.30
50.00
20.00
2
Gombe
132
23.40
20.01
16.01
3
Biu
132
4.80
1.50
1.20
4
Damboa
132
11.90
1.21
0.98
5
Maiduguri
132
76.60
32.50
26.01
6
Ashaka junction
132
30.00
9.10
6.00
7
Potiskum
132
28.00
15.5
8.90
8
Ashaka Cement Factory
132
3.51
1.40
2.40
9
Savannah
132
3.60
2.81
2.20
10
Numan
132
3.60
2.80
2.30
11
Yola
132
41.80
9.00
3.60
12
Bauchi
132
30.00
30.00
24.02
Source: [12]
3) STATCOM Device
The STATCOM device, a key component in this study, is modelled to enhance the network’s voltage stability
and reactive power control. The device is represented in the simulation environment with adjustable
parameters, allowing for placement at different Buses within the transmission network. This study focuses on
the placement of single STATCOM and double STATCOM devices, with the optimal location determined
using the Particle Swarm Optimization (PSO) technique.
4) PSO Optimization Technique
The PSO algorithm is applied to optimize the placement of STATCOM devices in the network. The algorithm
minimizes voltage deviation by iterating through possible placements of STATCOM at various Buses. The
PSO parameters, including inertia weight, acceleration constants, and particle velocity, were carefully tuned to
ensure convergence to the optimal solution. The objective function used for optimization was based on
minimizing voltage deviations from the nominal values across all Buses in the network.
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5) Simulation Data
The simulation requires several key datasets, including:
i. Transmission line parameters: The lines’ resistance, reactance, and susceptance.
ii. Bus data: Voltage levels, load capacities, and generation data for each Bus in the system.
iii. Transformer and load data: Specifications of the transformers and load characteristics at each Bus.
Fig. 1 gives the single line diagram of the studied network.
METHODOLOGY
The methodology for this study involved modelling and simulating the Gombe 132 kV, 12-Bus power network
to evaluate the impact of STATCOM devices on voltage stability. The following steps outline the power flow
analysis and optimization of STATCOM placement using PSO algorithm. Fig. 2 illustrates the methodology
employed for the study.
Fig. 1 Single Line Diagram of Gombe 132 kV Power Network [12]
Build test system in
MATLAB
Upload MATLAB file to
PSAT
Perform power flow
analysis in sequence and
using PSO algorithm
Repeat the procedure
including STATCOM
Compare and Analyze
the Result for all Cases
Analyze the obtained
results
Generate voltage
magnitude profile, real
and reactive power flow
with and without
STATCOM
Fig. 2 Methodology Block Diagram
1) Power Flow Analysis without STATCOM (Base Case)
The first step in the methodology is to simulate the existing power network without any compensation devices.
This served as the base case, providing the baseline data for voltage magnitudes, real and reactive power
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generation, and system losses across all Buses. This simulation helps establish the network’s voltage profile
and identify areas of potential voltage instability (Li et al., 2023). The power flow equations used in this step
are given in (1) and (2).
(1)
(2)
where
and
are the real and reactive power injections at Bus ,
and
are the voltage magnitude and
angle at Bus ,
and
are the conductance and susceptance between Buses and , and is the total
number of Buses in the system.
2) Power Flow Analysis with STATCOM
In the second step, STATCOM devices were introduced to enhance voltage regulation and reactive power
control. The STATCOM’s reactive power injection is modelled as given in (3):
(3)
where
is the reactive power provided by the STATCOM,
is the voltage magnitude at the
STATCOM’s location,
is the current injected or absorbed by the STATCOM and is the reactance of
the STATCOM device.
Each scenario involved placing a single STATCOM at various Buses (2–12, excluding the slack and PV Buses)
to observe their effect on voltage magnitudes. The objective was to optimize the location to ensure the best
voltage regulation.
Optimal Placement Using Particle Swarm Optimization (PSO)
The PSO algorithm was employed to determine the optimal placement for the STATCOM devices. PSO is a
swarm intelligence-based optimization technique that searches for the optimal solution by simulating the social
behaviour of particles (Kennedy & Eberhart, 1995). The objective function to be minimized in this case is the
voltage deviation from the nominal voltage (1.0 p.u.) at all Buses as expressed in (4):
(4)
where
is the voltage magnitude at Bus and is the nominal voltage. The goal is to minimise this deviation
and improve voltage in the process.
The steps for PSO are as follows:
1. Initialization: A population of particles (potential STATCOM placements) is initialized randomly within
the search space (the set of Buses).
2. Fitness Evaluation: The fitness of each particle is evaluated using the objective function .
3. Update Velocity and Position: Each particle updates its velocity and position based on its best-known
position (pbest) and the best-known position in the swarm (gbest):
… (5)
(6)
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where
and
are the new and current velocity of particles ,
and
are acceleration constants,
and
are random numbers between 0 and 1,
and
are the best positions of particle and the
swarm, respectively.
4. Termination Condition: The algorithm terminates after a predefined number of iterations (10 for a
single STATCOM and 30 for double STATCOM placement), returning the optimal placement of
STATCOM.
Fig. 3 gives the flowchart of the PSO algorithm.
Simulation Setup and Parameters
The PSO algorithm used the following parameters:
i. Inertia Weight (): A constant that decreases from 0.9 to 1.0 during the search process.
ii. Acceleration Constants
: These constants influence the effect of individual and
global best positions on particle velocity updates.
iii. Particle Population: The number of particles is set to 5, with random initial placements for the
STATCOM.
iv. Iterations: The algorithm runs 10 iterations for single STATCOM placement and 30 for double
STATCOM placement.
The single-line diagram of the Gombe 132 kV, 12-bus power network, shown in Fig. 1, was first modelled in
the PSAT/SIMULINK environment by dragging and placing components such as buses, generators, and
transmission lines. Transmission line parameters from Table 1 and bus data from Table 2 were input
accordingly. The completed power network model, developed using the PSAT GUI, is shown in Fig. 4.
3) Placement of STATCOM at Bus-2
A STATCOM was installed at Bus-2 (Fig. 5) and a power flow analysis was conducted to assess system
performance under compensation. The resulting report quantified key metrics at Bus-2—including voltage
magnitude, real/reactive power generation, losses, and total output—with the STATCOM operational. This
placement and analysis procedure was repeated for Buses 3, 4, and 6–12.
Initialise Problem
Parameters
Initialise Particles with
Random Positions and
Velocity
Solve the Target Problem
Evaluate Fitness Function
Update pbest
Update gbest
Is Stopping Condition Satisfied?
Return gbest and Optimal
Solution
Start
Stop
NO
YES
Update Position and
Velocity
Fig. 3: PSO algorithm flowchart
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Fig. 4: Network Model without STATCOM in PSAT MATLAB Environment
Fig. 5: Network model with STATCOM at Bus-2 in PSAT MATLAB environment
For each scenario—using a single STATCOM and using two STATCOMs—the power network was simulated
using the PSO algorithm in MATLAB. The results were compared against the base case (without
compensation) to evaluate the impact of STATCOM integration. At each bus where a STATCOM was installed
and power flow analysis was performed using PSAT, the real/reactive power generation and power losses were
recorded and tabulated. The effectiveness of the STATCOM was assessed by comparing the results with and
without compensation.
RESULTS
This section presents the simulation results for integrating a STATCOM into the Gombe 132 kV, 12-bus power
network. The STATCOM was placed on individual buses one at a time, and PSO algorithm was employed to
determine its optimal location for enhancing bus voltage magnitudes. The simulation results, with and without
STATCOM compensation using PSAT, were both tabulated and plotted.
A. Baseline Power System Performance (Without STATCOM)
For the system without STATCOM compensation, Table 3 presents the power flow results, including voltage
profiles and real and reactive power generation. A bar graph of bus voltage magnitudes versus bus numbers is
shown in Fig. 6. Bus voltages vary across the network, with Bus-12 (Fig. 6) recording the lowest voltage
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magnitude, and adjacent buses also experiencing low voltage levels. Most bus voltages fell outside the
acceptable range of 0.95 p.u. to 1.05 p.u. Bus-1 and Bus-5 exhibited higher voltage levels than the others. Bus-
1 is the slack bus that serves as the system reference with a predefined voltage magnitude and angle. At the
same time, Bus-5 is a PV bus where both real power and voltage are specified.
B. Results for Location of a Single STATCOM
This study explored the optimal placement of STATCOM across the buses to evaluate its power enhancement
capability. The STATCOM was placed at each bus individually (excluding the slack Bus-1 and PV Bus-5) to
identify the most effective location for improving bus voltage levels within the network.
C. Optimizing Voltage Regulation: A Comparison of STATCOM Placement at Buses 2 to 12
The results for placing a STATCOM at Bus-2, shown in Fig. 7, indicate an improvement in voltage levels
compared to the uncompensated system; however, this placement does not achieve optimal voltage regulation.
While the voltage magnitudes at most buses were enhanced, many still remained outside the acceptable range
of 0.95 p.u. to 1.05 p.u. This suggests that Bus-2, while offering some improvement, is not the ideal location
for optimal voltage stabilization within the network. In comparison, the placement of the STATCOM at Bus-6,
illustrated in Fig. 7, demonstrates a more significant voltage improvement across all buses, with the voltages at
buses 1 through 8 remaining within the desired voltage range. Although buses 9, 10, 11, and 12 still showed
voltage levels below the lower bound of 0.95 p.u., their values were improved compared to the base case,
showing that Bus-6 provided a better overall voltage regulation than Bus-2.
Table Iii Voltage Magnitude Without Statcom (Base Case)
Bus No
V m (p.u)
Phase (Deg.)
P gen (p.u)
Q gen (p.u)
P Load (p.u)
Q Load P.u)
1
1.06400
0.00000
0.67389
0.89448
0.00000
0.00000
2
0.95096
-4.75580
0.00000
0.00000
0.20000
0.16000
3
0.99012
-4.41180
0.00000
0.00000
0.01500
0.01200
4
0.99730
16.42700
0.00000
0.00000
0.01200
0.00980
5
1.06000
21.82700
0.80000
0.25553
0.32500
0.26000
6
0.88295
-8.65600
0.00000
0.00000
0.08900
0.05000
7
0.83760
-10.97660
0.00000
0.00000
0.15500
0.08900
8
0.86422
-9.74990
0.00000
0.00000
0.08900
0.05000
9
0.85091
-6.85091
0.00000
0.00000
0.02800
0.02200
10
0.77530
-10.26650
0.00000
0.00000
0.03005
0.08453
11
0.75582
-11.46870
0.00000
0.00000
0.08033
0.03213
12
0.74462
-8.74370
0.00000
0.00000
0.25990
0.20792
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Fig. 6 Voltage Magnitude Without STATCOM (Base Case)
Fig. 7: Voltage Magnitude with Single STATCOM at Bus-2
In Fig. 8, the addition of STATCOM at Bus-7 significantly enhances the voltage profile of the network,
especially when compared to the base case scenario. Most buses within the system, particularly buses 1 to 8,
remain within the defined voltage range, illustrating a more stable and improved voltage regulation. The results
suggest that Bus-7 is a superior location for placing the STATCOM, providing better overall performance
across the network, as opposed to Bus-2. Although the voltage improvement for buses 9, 10, 11, and 12 does
not fully meet the 0.95 p.u. lower limit, their enhancement over the uncompensated scenario marks Bus-7 as
the preferred location for achieving more consistent voltage levels throughout the system.
D. Results for Location of STATCOM for Real and Reactive Power Control
Table IV presents the results of reactive power generation by the power system with the STATCOM positioned
at each bus, with the corresponding plot shown in Fig. 9. As demonstrated in Table 2 and Fig. 2, the reactive
power generation varies depending on the location of the STATCOM. Notably, the reactive power increases at
the buses where the STATCOM was installed, indicating that the device effectively supplies the required
reactive power to the specific points in the system where it is needed. Table V give a general comparative
analysis of the proposed study with some existing works.
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Fig. 8: Voltage Magnitude with a Single STATCOM at Bus-7
Table Iv Total Power Generation On The Network With Statcom
STATCOM at Buses
Total real power generation (p.u)
Total reactive power generation (p.u)
2
1.407
1.158
3
1.475
1.142
4
1.474
1.148
6
1.509
1.088
7
1.503
1.100
8
1.501
1.105
9
1.505
1.088
10
1.497
1.086
11
1.498
1.093
12
1.507
1.043
Fig. 9: Total Real and Reactive Power Generation with Single STATCOM at Each Bus
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Table V Comparative Performance Analysis
Reference
Method Used
Result Obained
Research Gap
[13]
Particle Swarm
Optimization-based
power flow control in
power systems.
Demonstrated effective power
flow control in systems using
PSO, showing improvements
in voltage regulation and
stability.
Limited application to real-world,
large-scale systems. Needs more
focus on complex power systems
with dynamic conditions and the
integration of multiple FACTS
devices.
[14]
Optimization of
FACTS devices for
voltage stability in
power grids using
PSO.
Found that optimized
placement of FACTS devices
improves voltage stability and
reduces transmission losses.
Lack of detailed analysis for specific
regions, particularly in the Nigerian
context. Further research needed on
PSO applications in 132 kV
transmission networks.
[15]
Power flow
optimization using
Particle Swarm
Optimization.
Applied PSO to optimize
power flow, achieving
significant improvements in
voltage stability and network
performance.
Research did not focus on specific
network designs or detailed case
studies of Nigerian power systems,
especially smaller, regional networks.
[16]
Evaluated STATCOM
performance in
mitigating voltage
instability.
Identified STATCOM’s
effectiveness in voltage
regulation, showing
improvements in reactive
power control across the
network.
Research focused mainly on
theoretical models with limited
application to actual Nigerian power
grid conditions, including 132 kV
networks.
[17]
Particle Swarm
Optimization
algorithm for
optimization in power
systems.
Introduced PSO as an
optimization technique,
demonstrating its ability to find
optimal solutions in complex,
nonlinear systems.
Need for more focused applications
in power system optimization and
performance assessment, particularly
in developing country grids like
Nigeria’s.
[18]
Analysis of
transmission
infrastructure and
power quality issues in
Nigeria.
Identified key power quality
issues including voltage
instability and proposed
solutions involving FACTS
devices.
Lacks a detailed focus on PSO or
optimization methods applied to
Nigerian 132 kV networks for real-
world solutions, particularly
regarding STATCOM placement.
[19]
Focused on voltage
instability and
mitigation strategies in
Nigerian power
systems.
Showed that integrated
solutions with FACTS devices
could stabilize voltage and
reduce outages in Nigerian
grids.
More in-depth research is needed on
the use of optimization algorithms for
FACTS device placement within
Nigeria's complex 132 kV
transmission networks.
[20]
Voltage Instability
Prediction of Nigerian
330kV Network Using
Arithmetic Moving
Average and Predictive
Optimizer Technique.
Applied a predictive
optimization technique to
identify voltage instability in
the Nigerian 330kV network,
using a combination of moving
averages for improved
Limited focus on optimization
techniques like PSO for predicting
and mitigating voltage instability in
large, interconnected power systems.
Further research is needed on
integrating PSO with predictive
models for voltage stability
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accuracy.
enhancement.
[21]
Optimal placement of
STATCOM devices
using genetic
algorithms.
Found that genetic algorithms
can successfully optimize
STATCOM placement to
improve voltage stability and
power flow control.
Limited focus on PSO as an
alternative optimization method for
FACTS device placement. Further
research needed on the comparison of
PSO and genetic algorithms in
voltage regulation in transmission
systems.
[22]
PSO-based
optimization for
voltage regulation in
transmission networks.
Confirmed PSO's effectiveness
in optimizing voltage
regulation by minimizing
voltage deviations across the
network.
More research needed to explore the
use of PSO in optimizing the
placement of multiple STATCOMs
and other FACTS devices in large,
interconnected networks. Further
analysis is needed for Nigerian
transmission systems.
[23]
Optimizing Power
Losses and Voltage
Profiles through
Simultaneous
Distribution Network
Reconfiguration and
DG Placement Using a
Hybrid CDOA-PSO
Algorithm.
Optimized power losses and
voltage profiles in a Nigerian
distribution network.
Research primarily focused on
distribution networks, lacking
integration with FACTS devices for
voltage regulation. Further research
needed on hybrid optimization
algorithms for both distribution and
transmission networks.
[24]
A Novel Combination
of Genetic Algorithm,
Particle Swarm
Optimization, and
Teaching-Learning-
Based Optimization
for Distribution
Network
Reconfiguration in
Case of Faults.
Applied a hybrid approach
combining PSO and genetic
algorithms for fault-tolerant
distribution network
reconfiguration.
Limited exploration of PSO’s
potential in enhancing voltage
regulation in power transmission
systems like Nigeria’s 132 kV
networks
Proposed
Study
(2025)
The study evaluated
the effectiveness of
STATCOM devices for
voltage profile
enhancement in the
Gombe 132 kV, 12-
Bus power network,
using PSO to
determine the optimal
placement.
Optimized placement of
STATCOM at Bus-12
significantly improves voltage
levels, maintaining them within
the IEEE standard limits (0.95
≤ V ≤ 1.05 p.u.). Enhanced
reactive power control and real
power generation were
observed across all buses.
Limited research specifically
applying PSO to optimize
STATCOM placement in Nigeria’s
132 kV, 12-Bus network, highlighting
the need for further studies on real-
world applications in the Nigerian
grid.
CONCLUSION
The study analysed voltage enhancement in the Gombe 132 kV power network using STATCOM and
optimized its placement across 12 buses. A single STATCOM was sequentially placed at each bus, and PSO
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algorithm was employed to determine the optimal siting of one or two STATCOMs. The results, comparing
scenarios with and without STATCOM, reveal that proper placement of STATCOM at the optimal location
significantly improves voltage levels within the operating range of 0.95 ≤ V ≤ 1.05 p.u., effectively controlling
both real and reactive power flows. The best network performance was achieved by placing a single
STATCOM at Bus-1wA2, and the implementation of STATCOM contributed to enhanced real power
generation.
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