scholarly journals Application of a Particle Swarm Optimization for Improving Voltage Profile with Distributed Generation: A Case Study of 33/0.415KV Abuja Airport Injection Substation

2019 ◽  
Vol 4 (3) ◽  
pp. 100-106
Author(s):  
Stephen Oodo ◽  
Felix Sanjo Owolabi
Keyword(s):  
Particle Swarm Optimization ◽  
Distributed Generation ◽  
Power Distribution ◽  
Distribution Network ◽  
Mathematical Formulation ◽  
Particle Swarm ◽  
Voltage Profile ◽  
Swarm Optimization ◽  
System A ◽  
The Impact

The important of electric power distribution is to have centralized plants distributing electricity through Distributed generation (DG) which reduces the cost of maintenance on transmission and distribution station and also improve voltage profile. This research paper present the application of generation based on Biogas power renewable energy source to the Distribution network and how it stabilizes the network by normalizing the fluctuating voltage profile at the distribution end of power system. A Particle Swarm Optimization (PSO) model was performed and evaluation of the impact of the DG by stimulating the developed model in the system. A mathematical formulation and optimization algorithm was performed using the MATLAB/Simulink program. The results obtained were correction of the faulty buses voltages and stable power supply which is 29.4% better than the conventional one. The result shows the implementation of the optimisation technique has improved the energy efficiency of the distribution network.

Power Distribution Network Reconfiguration by Using EPSO for Loss Minimizing

2014 ◽  
Vol 699 ◽  
pp. 809-815 ◽  
Author(s):  
Mohamad Fani Sulaima ◽  
Mohd Hafiz Jali ◽  
Wan Mohd Bukhari ◽  
M.N.M. Nasir ◽  
Hazriq Izzuan Jaafar
Keyword(s):  
Particle Swarm Optimization ◽  
Power Distribution ◽  
Distribution Network ◽  
Heuristic Method ◽  
Particle Swarm ◽  
Computational Time ◽  
Network Reconfiguration ◽  
Voltage Profile ◽  
Power Distribution Network ◽  
Swarm Optimization

Due to the complexity of modern power distribution network, a hybridization of heuristic method which is called as Evolutionary Particle Swarm Optimization (EPSO) is introduced to identify the open and closed switching operation plans for network reconfiguration. The objectives of this work are to reduce the power losses and improve the voltage profile in the overall system meanwhile minimizing the computational time. The proposed combination of Particle Swarm Optimization (PSO) and Evolutionary Programming (EP) is introduced to make it faster in order to find the optimal solution. The proposed method is applied and it impacts to the network reconfiguration for real power loss and voltage profiles is investigated respectively. The proposed method is tested on a IEEE 33-bus system and it is compared to the traditional PSO and EP method accordingly. The results of this study is hoped to help the power engineer to configure the smart and less lossed network in the future.

scholarly journals Multi-Objective Power Distribution Network Reconfiguration using Chaotic Fractional Particle Swarm Optimization

2021 ◽  
Vol 19 (1) ◽  
pp. 43-50
Author(s):  
Yashar Mousavi ◽  
Mohammad Hosein Atazadegan ◽  
Arash Mousavi
Keyword(s):  
Particle Swarm Optimization ◽  
Power Distribution ◽  
Distribution Network ◽  
Particle Swarm ◽  
Superior Performance ◽  
Network Reconfiguration ◽  
Power Distribution Network ◽  
Swarm Optimization ◽  
Distribution Network Reconfiguration ◽  
Multi Objective

Optimization of power distribution system reconfiguration is addressed as a multi-objective problem, which considers the system losses along with other objectives, and provides a viable solution for improvement of technical and economic aspects of distribution systems. A multi-objective chaotic fractional particle swarm optimization customized for power distribution network reconfiguration has been applied to reduce active power loss, improve the voltage profile, and increase the load balance in the system through deterministic and stochastic structures. In order to consider the prediction error of active and reactive loads in the network, it is assumed that the load behaviour follows the normal distribution function. An attempt is made to consider the load forecasting error on the network to reach the optimal point for the network in accordance with the reality. The efficiency and feasibility of the proposed method is studied through standard IEEE 33-bus and 69-bus systems. In comparison with other methods, the proposed method demonstrated superior performance by reducing the voltage deviation and power losses. It also achieved better load balancing.

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