Particle Swarm Optimization with multi-ring topology applied to the optimization of power transmission line parameters

2015 ◽  
Author(s):  
Sergio Arruda ◽  
Bernardo Alvarenga ◽  
Wesley Calixto
Keyword(s):  
Particle Swarm Optimization ◽  
Transmission Line ◽  
Power Transmission ◽  
Particle Swarm ◽  
Power Transmission Line ◽  
Ring Topology ◽  
Swarm Optimization

Comparison of SVC and TCSC Installation in Transmission Line with Loss Minimization and Cost of Installation via Particle Swarm Optimization

2015 ◽  
Vol 785 ◽  
pp. 495-499
Author(s):  
Siti Amely Jumaat ◽  
Ismail Musirin
Keyword(s):  
Particle Swarm Optimization ◽  
Transmission Line ◽  
Objective Function ◽  
Transmission Loss ◽  
Particle Swarm ◽  
Static Var Compensator ◽  
Loss Minimization ◽  
Swarm Optimization ◽  
The Cost ◽  
Thyristor Controlled Series Compensator

The paper presents a comparison of performance Static Var Compensator (SVC) and Thyristor Controlled Series Compensator (TCSC) with objective function to minimize the transmission loss, improve the voltage and monitoring the cost of installation. Simulation performed on standard IEEE 30-Bus RTS and indicated that EPSO a feasible to achieve the objective function.

scholarly journals An Elitist Learning Particle Swarm Optimization With Scaling Mutation and Ring Topology

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 78453-78470 ◽  
Author(s):  
Guangzhi Xu ◽  
Xinchao Zhao ◽  
Tong Wu ◽  
Rui Li ◽  
Xingmei Li
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Ring Topology ◽  
Swarm Optimization

Power Transmission and Transformation Project Establishing and Decision-Making Based on ASU-MOPSO Algorithm

2014 ◽  
Vol 521 ◽  
pp. 521-529 ◽  
Author(s):  
Ke Sun ◽  
Kai Xu ◽  
Zhao Ming Zheng ◽  
Xiao Yu Ding ◽  
Ke Sun ◽  
...  
Keyword(s):  
Decision Making ◽  
Particle Swarm Optimization ◽  
Euclidean Distance ◽  
Power Transmission ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Computing Complexity ◽  
Crowding Distance ◽  
Decision Making Problem

This paper demonstrates an asynchronous-stepwise updated strategy multi-objective particle swarm optimization (ASU-MOPSO) algorithm to improve the convergence and diversity of the multi-objective particle swarm optimization. In the process of the elite reduction, we utilize the asynchronous grid strategy to filter particles since this strategy has lower computing complexity. Meanwhile, the stepwised Euclidean crowding distance strategy is presented to filter particles within every grid, which uses the sum of the Euclidean distance with the two nearest particles to replace of the traditional crowding distance. This strategy can avoid the destruction of distribution diversity. Finally, our algorithm is successfully applied for the actual power transmission and transformation project establishing and decision-making problem. Comparing with traditional MOPSO based on crowding distance strategy and grid strategy, our algorithm can obtain the better solution.

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