Hunter-Prey Optimization Framework for UPQC Allocation to Improve Efficiency and Voltage Reliability of Power Distribution Network

Akash Kumar Bansal; Ram Narayan Singh

doi:doi:10.11648/j.jeee.20261401.13

Research Article |

| Peer-Reviewed

Hunter-Prey Optimization Framework for UPQC Allocation to Improve Efficiency and Voltage Reliability of Power Distribution Network

Akash Kumar Bansal^*

, Ram Narayan Singh

Published in Journal of Electrical and Electronic Engineering (Volume 14, Issue 1)

Received: 27 December 2025 Accepted: 12 January 2026 Published: 29 January 2026

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Abstract

Maintaining power quality in the power distribution network is a major concern due to the increasing penetration of nonlinear and complex equipment. Unified Power Quality Conditioners (UPQCs) have been widely used as effective compensating devices to mitigate voltage instability and current distortions. However, the major challenge lies in selecting the optimal location and rating of the UPQC in the distribution network. Proper placement of the UPQC significantly improves overall system efficiency by enhancing the voltage profile, reducing active power losses, and improving cost effectiveness. In this study, the Hunter-Prey Optimization (HPO) algorithm is employed to determine the optimal location and rating of the UPQC in the distribution network. The objective function combines active power loss, voltage deviation, and UPQC installation cost while satisfying network and control constraints. The proposed framework is evaluated on the IEEE 33-bus and IEEE 69-bus distribution systems. Simulation results demonstrate that the HPO algorithm efficiently identifies the optimal UPQC placement and rating, resulting in a significant reduction in active power losses of 60% for the IEEE 33-bus system and 93.5% for the IEEE 69-bus system, along with a notable improvement in voltage profiles compared to the system without UPQC.

Published in	Journal of Electrical and Electronic Engineering (Volume 14, Issue 1)
DOI	10.11648/j.jeee.20261401.13
Page(s)	21-33
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2026. Published by Science Publishing Group

Keywords

Unified Power Quality Conditioners, Voltage Profile, Active Power Loss, Hunter-Prey Optimization Algorithm, IEEE 33-bus, IEEE 69-bus

1. Introduction

In recent years, power quality has become a major concern in distribution networks, as disturbances such as voltage sags

[1]

and high-power losses

[2]

significantly affect overall system performance. These disturbances are majorly caused due to the rapid rise of the non-linear and dynamic loads such as modern electronic devices, industrial machinery and many more electrical power consuming components in the real-world applications. Conversely, the integration of the distributed generation systems such as solar

[3, 4]

, wind

[5]

has made the situation more complex by introducing additional challenges in the network

[6]

. Together, the combination of all the above-mentioned components has significantly introduced the issues in the power distribution network such as harmonic distortions, voltage imbalances and higher electrical power losses, making the entire distribution network less reliable and degrading the performance of the system.

In order to maintain the voltage in the network in a stable way, reduce the electrical power losses and deliver the electrical power efficiently by considering the challenging issues in the network. There is a need for the efficient compensation devices such as Unified Power Quality Conditioners (UPQC) to be installed in the network

[7, 8]

along with the intelligent optimization techniques. When these kinds of advanced devices and techniques are applied on the distribution network, this leads to the identity and minimize the disturbances in the power system and make sure that the network operates under a stable, smooth environment along with better efficiency.

The primary function of the Unified Power Quality Conditioner (UPQC) is to improve overall power quality in the distribution system. Its architecture mainly consists of the series and shunt compensators connected as a single unit in order to handle the multiple power quality issues at the same time. In detail, the series compensator primary function is to correct and control the voltage-related disturbances such as voltage variations

[9]

, flickers (i.e., frequency fluctuations) and swells (i.e., sudden increase) and making sure that the voltage supplied to consumers is within prescribed limits. Conversely, the shunt compensator mainly deals with the current related issues such as reducing the current harmonics

[10]

that are caused by the dynamic and inductive loads and also balances the reactive power.

Even though UPQC has the better advantages, the major challenge lies in the optimal placing of the UPQC in the network and how its capacity is configured. If the UPQC is not placed at an optimal location and thus fails in its operation. This includes poor voltage regulation, not handling the harmonics efficiently and fails to improve the overall system performance.

The emergence of advanced optimization has solved the issue of selecting the suitable location for placing the UPQC in the network. Traditional optimization methods such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA) have been commonly used to determine the suitable location to install the UPQCs in the power distribution network. Although these optimization methods offer notable advantages, they often suffer from limitations such as premature convergence and increased computational burden. This means instead of searching for the optimal location in the large distribution network, these optimization methods settle for the suboptimal solution and also involve the high computational and installation cost.

In order to address the issues that are caused in the traditional optimization methods, this study has introduced the Hunter-Prey Optimization (HPO) Algorithm and it is a recently developed bio-inspired metaheuristic technique. The main advantage of using this technique is because of its strong capabilities and dynamic balance between the exploration (i.e., searching for the new possibilities) and exploitation (i.e., refining good solutions). Because of these unique advantages, HPO is better suited for the optimal allocation and sizing of the UPQCs devices in the large and complex power distribution network.

In this study, in order to evaluate the optimization techniques for the distribution network, IEEE 33-bus and IEEE 69-bus have been effectively utilized. IEEE 33-bus system has the advantage of mimicking the medium-sized distributed radial network with moderate load density. Conversely, the IEEE 69-bus is used because of its complex network topology. By implementing the proposed Hunter-Prey optimization based UPQC allocation methods effectively on these two distributed systems such as IEEE 33-bus and IEEE 69-Bus has provided the key insights into how the optimization methods have performed, while also considering the how the annual cost has been minimized by the efficient loss reduction in the network.

2. Related Works

Many of the existing approaches have been effectively used to mitigate the issues that are raised due to the problem of selecting the optimal location to install the compensating devices in the distribution network to reduce the active power loss and enhance the voltage stability. Heuristic and Metaheuristic techniques such as Genetic Algorithm (GA)

[11]

and Particle Swarm Optimization (PSO)

[12, 13]

have shown significant performance in the selection of proper installment of the capacitor banks and UPQC in the network. But the major drawback of these methods is Genetic Algorithm (GA) is able to take more convergence and PSO has the limitation to settle for the local optimal solution in highly nonlinear spaces. Some of the other algorithms also such as Differential Evaluation (DE)

[16]

, Ant Colony Optimization (ACO) and Whale Optimization Algorithm (WOA)

[14, 15]

have shown significant performance in applying the reactive power compensation but these algorithms have compromises between the exploitation and exploration efficiency.

This study using the Hunter-Prey Optimization (HPO) Algorithm has significant performance by efficiently balancing between the exploration and exploitation, allowing us to search for the global optimal even though the optimal solution is available. This research has used this algorithm to select the optimal location of the UPQC in the network and achieved the superior outcomes in terms of reducing the active power loss, voltage profile improvement and convergence speed.

3. Research Methodology

3.1. System Design and Modeling

In this study, the analysis of the proposed framework has been carried out using the two recognized benchmarked distribution systems such as IEEE 33-bus and IEEE 69-bus distribution systems. The main objective behind choosing these two systems, because majority of the studies and research has evaluated the performance of the new optimization and power quality improvement techniques. The entire modeling of these systems has been carried through the MATALAB/Simulink environment. Because this environment provides a flexible and reliable platform for simulating the real-world electrical network behavior.

In these models, each bus which is in the distribution system is defined by the significant parameters such as active power demand (P), reactive power demand (Q). In addition, the branch data consist of the electrical parameters such as the resistance (R), reactance (X) and line length (L). These parameters play an important role in influencing the voltage profile and the loss characteristics of the system.

Initially, the analysis of the power in the IEEE 33-bus and IEEE 69-bus system has been carried out without placing the UPQCs in the network. This kind of analysis is important in calculating the key parameters. Total active power loss, it means the power which has been wasted as a heat is the distribution network. Bus voltage magnitudes, it means the voltage which has been across each of the bus in the distribution network. Annual cost, it means the amount which has been involved for the installation, operation and maintenance cost of the UPQCs in the network in order to maintain the voltage and loss in safer limits.

In order to maintain the electrical power network, by installing the UPQCs at the suitable locations in the network, it could be achievable. Unified Power Quality Controllers (UPQCs) have mainly been developed by the integration of the shunt compensators and series compensators into a single unit. The proper balance and coordination between these two compensators allow the UPQCs to maintain the voltage and current profiles in the network at the stable levels.

The main objective of this optimized framework in this study is to minimize the total active power loss in the network, maintaining the voltage levels in the network at the acceptable limits and also keeping the installation and maintenance cost of the UPQCs as low as possible.

The function can be mathematically represented as below:

P_{Loss} = \sum_{i = 1}^{L} P_{i}

(1)

Where

P_{i}

= Active Power loss in the

i_{t h}

line

P_{Loss}

= Total Active Power Loss

Minimize : f = P_{Loss} + α * Σ (V_{i} - V_{ref})^{2} + β * {Cost}_{UPQC}

(2)

Where,

V_{i}

= Voltage at the bus ‘i’

V_{ref}

= Reference Voltage

α

β

= Weighting Factors

{Cost}_{UPQC}

= Total cost associated with the installation, operation and maintenance of the UPQC

In order to ensure the obtained solution has been technically and practically feasible some of the constraints need to be applied such as the balance in the power flow, the bus voltage magnitude has been maintained at the

\pm

5% of the nominal voltages and also reactive power injection by the UPQCs must not be exceeded. Together all these constraints have played a vital role in the voltage reliability and cost-effectiveness through the UPQC allocation in the network.

3.2. Hunter-Prey Optimization Framework for UPQC Allocation

This study uses the Hunter-Prey Optimization (HPO) algorithm in order to allocate the optimal allocation of the UPQC device in the distribution network. The HPO has the strong capabilities and the proper balance between the exploration and exploitation, which leads to the search efficiency and stable convergence performance.

Initially, the HPO algorithm was able to generate the initial population and each of these represents the possible UPQC placement configuration and it is modeled as an agent, classified either hunter or prey. Then each of these agents has been further evaluated using the objective function ‘f’. The agents which have achieved the better score have been represented as the better solution. During each iteration, the prey agent moves away from the hunter agent on the basis of the probability factor. This kind of mechanism has helped in expanding the exploration of the search space. This means for UPQC allocation in the network, it helps examine the alternative best bus location and capacity settings.

Then the hunter agent is moved towards the prey agent by using the adaptive pursuit equations by considering the both current solution and the relative distance. This step further enhances precise tuning of the UPQC placement in the network with the better sizing. After each iteration, the position and function values of both the hunter and prey has been evaluated. The agent which has obtained the better values and it is replaced and highlights that it is the better solution. Then this solution is identified as the global best position and it represents the most effective UPQC allocation observed in the distribution network during the optimization cycle.

This kind of iteration has been continued until it reaches the convergence criteria satisfied. Then the solution which is identified has been considered as the global best position in order to place the UPQC devices at the bus location and rating of the device. Finally, the output has been used to update the power flow model for the performance evaluation such as reduction in the active power loss, voltage improvement and annual cost in the distribution network.

Table 1. Pseudocode of Hunter–Prey Optimization (HPO) for Optimal UPQC Allocation.

Step	Procedure
Input	Npop, MaxIter, System data (bus, line), constraints, weighting factors (α, β)
Output	Optimal UPQC location and rating, minimum fitness value
1	Randomly initialize hunter and prey populations
2	Assign random UPQC location and rating to each agent
3	Compute total active power loss (PLoss)
4	Compute annual UPQC cost (COST<sub>UPQC</sub>)
5	Evaluate objective function f
6	Identify initial best solution and fitness
7	For t = 1 to MaxIter For each prey agent Generate escape probability P<sub>e</sub> = rand()
7.1	If P<sub>e</sub> < 0.5, move randomly within bounds
7.2	Else, move away from nearest hunter
7.3	For each hunter agent
7.4	Identify nearest prey using distance and fitness
7.5	Update hunter position toward prey
7.6	Apply adaptive step scaling
8	Evaluate fitness of all agents
9	Replace weaker agents with improved solutions
10	Store best fitness for convergence
End	Output final optimal UPQC allocation

[3]	Elomari, Y., Norouzi, M., Marín-Genescà, M., Fernández, A., & Boer, D. (2022). Integration of solar photovoltaic systems into power networks: A scientific evolution analysis. Sustainability, 14(15), 9249.
[4]	Bhatt, P. K. (2022). Solar Energy Integration and Potential Challenges in Distribution Network: A Comprehensive Review. Journal of Energy Research and Reviews, 42-65.

[7]	Qasim, A. Y., Tahir, F. R., & Alsammak, A. N. B. (2024). Improving Power Quality in Distribution Systems Using UPQC: An Overview. Journal European Journal of Automation, 57(2): 311-322. https://doi.org/10.18280/jesa.570201 View Article
[8]	Dashtdar, M., Bajaj, M., Hosseinimoghadam, S. M. S., Sami, I., Choudhury, S., Rehman, A. U., & Goud, B. S. (2021). Improving voltage profile and reducing power losses based on reconfiguration and optimal placement of UPQC in the network by considering system reliability indices. International Transactions on Electrical Energy Systems, 31(11), e13120.

[12]	Tahir, M. J., Rasheed, M. B., & Rahmat, M. K. (2022). Optimal placement of capacitors in radial distribution grids via enhanced modified particle swarm optimization. Energies, 15(7), 2452.
[13]	Ahmadi Kamarposhti, M., Ghandour, R., Abdel-Aty, M., Hafez, M., Alfiras, M., Alkhazaleh, S.,... & Solyman, A. (2025). Optimizing capacitor bank placement in distribution networks using a multi-objective particle swarm optimization approach for energy efficiency and cost reduction. Scientific Reports, 15(1), 12332.

[14]	Zhang, J., Zhang, T., Zhang, G., Wang, D., & Kong, M. (2023). Using the whale optimization algorithm to solve the optimal reactive power dispatch problem. Processes, 11(5), 1513.
[15]	Baby, H., Jayakumar, J., Mathew, M., Hussien, M. G., & Kumar, N. M. (2025). Analysis of reactive power loadability and management of flexible alternating current transmission system devices in a distribution grid using whale optimization algorithm. IET Renewable Power Generation, 19(1), e12661.

System	Case	Minimum Voltage (p.u)	Active Power Loss (kW)	Annual Cost ($)
IEEE 33-Bus	Without UPQC	0.062301	2386.6614	400959.1086
IEEE 33-Bus	With UPQC	0.81029	961.1745	161477.3222

System	Case	Minimum Voltage (p.u)	Active Power Loss (kW)	Annual Cost ($)
IEEE 69-Bus	Without UPQC	0.073138	7844.0968	1317808.2615
IEEE 69-Bus	With UPQC	0.36511	500.5584	84093.8133

UPQC	Unified Power Quality Conditioner
HPO	Hunter-Prey Optimization
PSO	Particle Swarm Optimization
GA	Genetic Algorithm
TLBO	Teaching Learning-Based Optimization
AOA	Arithmetic Optimization Algorithm
DE	Differential Evolution
ACO	Ant Colony Optimization
WOA	Whale Optimization Algorithm
DG	Distributed Generation
PV	Photovoltaic
p.u	Per Unit
kW	Kilowatt
kVAr	Kilovolt-Ampere Reactive
IEEE	Institute of Electrical and Electronics Engineers
P_Loss	Total Active Power Loss
P_i	Active Power Loss of i-th Line
V_i	Voltage at i-th Bus
V_ref	Reference Voltage
α, β	Weighting Factors
MATLAB	Matrix Laboratory
O&M	Operation and Maintenance

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[4]	Bhatt, P. K. (2022). Solar Energy Integration and Potential Challenges in Distribution Network: A Comprehensive Review. Journal of Energy Research and Reviews, 42-65.
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Bus Number	Size (kVAr)
33	1.2000
8	1.2000
17	0.6158

Bus Number	Size (kVAr)
50	1.1768
9	1.2000
48	1.2000
37	1.2000