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2025, Vol: 32, Issue: 1
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Original Research
Online Published: 09 Sep 2021
 


Optimal Integration of Distributed Generation for Power Loss Reduction and Voltage Profile Enhancement using Small Population Particle Swarm Optimization

I. Musa,; H. Salawu, A. D. Usman.


Abstract
In this paper, a meta-heuristic Small Population Particle Swarm Optimization (SPPSO) is proposed for Distributed Generation (DG) integration with the objective of power loss reduction and system voltage profile improvement. One of the major setbacks of classical particle swarm optimisation (PSO) is its computational complexity and the SPPSO is employed to address this setback. The classical PSO is also implemented in this study to provide a realistic and feasible comparison for the performance of the two algorithms (Classical PSO & SPPSO) in optimal DG integration problem. The algorithms are tested on two standard 33 and 69 bus radial distribution systems. In each test network, two scenarios are considered for DG sizing and location; DG operation at unity power factor (fit and forget approach) and DG operation at pre-specified practical power factor less than unity. The SPPSO algorithm finds the optimal or near optimal solution to the problem at less computational cost associated with the use of the classical version of the algorithm. The connection of DG to 33bus network reduced the power loss by 47.40% at unity p.f and 67.71% at 0.85 lagging p.f. While, for the 69bus, the reduction in power loss at unity power factor and 0.85 p.f are respectively 63.11% and 89.4%. Thus, demonstrating the benefits accrue to Distribution Network Operators to operate their generators at a pre-specified practical power factors less than unity.

Key words: Classical PSO; Distributed Generation; Small Population PSO; Power system optimization; Voltage support


 
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How to Cite this Article
Pubmed Style

Musa, I, Usman HSAD. Optimal Integration of Distributed Generation for Power Loss Reduction and Voltage Profile Enhancement using Small Population Particle Swarm Optimization . NJE. 2021; 28(2): 7-14.

Web Style

Musa, I, Usman HSAD. Optimal Integration of Distributed Generation for Power Loss Reduction and Voltage Profile Enhancement using Small Population Particle Swarm Optimization . https://www.njeabu.com.ng/?mno=122501 [Access: September 12, 2024].

AMA (American Medical Association) Style

Musa, I, Usman HSAD. Optimal Integration of Distributed Generation for Power Loss Reduction and Voltage Profile Enhancement using Small Population Particle Swarm Optimization . NJE. 2021; 28(2): 7-14.


Vancouver/ICMJE Style

Musa, I, Usman HSAD. Optimal Integration of Distributed Generation for Power Loss Reduction and Voltage Profile Enhancement using Small Population Particle Swarm Optimization . NJE. (2021), [cited September 12, 2024]; 28(2): 7-14.


Harvard Style

Musa,, I. & Usman, . H. S. A. D. (2021) Optimal Integration of Distributed Generation for Power Loss Reduction and Voltage Profile Enhancement using Small Population Particle Swarm Optimization . NJE, 28 (2), 7-14.


Turabian Style

Musa,, I., and H. Salawu, A. D. Usman. 2021. Optimal Integration of Distributed Generation for Power Loss Reduction and Voltage Profile Enhancement using Small Population Particle Swarm Optimization . Nigerian Journal of Engineering, 28 (2), 7-14.


Chicago Style

Musa,, I., and H. Salawu, A. D. Usman. "Optimal Integration of Distributed Generation for Power Loss Reduction and Voltage Profile Enhancement using Small Population Particle Swarm Optimization ." Nigerian Journal of Engineering 28 (2021), 7-14.


MLA (The Modern Language Association) Style

Musa,, I., and H. Salawu, A. D. Usman. "Optimal Integration of Distributed Generation for Power Loss Reduction and Voltage Profile Enhancement using Small Population Particle Swarm Optimization ." Nigerian Journal of Engineering 28.2 (2021), 7-14. Print.


APA (American Psychological Association) Style

Musa,, I. & Usman, . H. S. A. D. (2021) Optimal Integration of Distributed Generation for Power Loss Reduction and Voltage Profile Enhancement using Small Population Particle Swarm Optimization . Nigerian Journal of Engineering, 28 (2), 7-14.


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