MODIFIED PARTICLE SWARM OPTIMIZATION FOR AN OPTIMAL FEEDER-SWITCH RELOCATION

2008 ◽  
Vol 17 (02) ◽  
pp. 401-409
Author(s):  
YUTTHAPONG TUPPADUNG ◽  
WERASAK KURUTACH
Keyword(s):  
Particle Swarm Optimization ◽  
Weight Function ◽  
Distribution System ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Radial Distribution System ◽  
Modified Particle Swarm Optimization ◽  
Inertia Weight ◽  
Weight Method ◽  
Interruption Costs

This paper presents an optimal feeder-switch relocation that is applied by modified Particle Swarm Optimization (MPSO). An inertia weight in Particle Swarm Optimization (PSO) is modified to find the best patterns of distribution configuration. MPSO performance is evaluated by comparison with the conventional inertia weight method. Six different benchmark functions with asymmetric initial range settings are selected as testing functions. A nonlinear inertia weight function is applied in this paper. The results of the experiment illustrate the advantage of MPSO. The optimal feeder-switch relocation in a radial distribution system is used to evaluate the MPSO performance. The results show that MPSO can identify suitable switch locations, based on minimum customer interruption costs.

scholarly journals Optimal parameter values of PID controller for DC motor based on modified particle swarm optimization with adaptive inertia weight

2021 ◽  
Vol 1 (2 (109)) ◽  
pp. 35-45
Author(s):  
Mohammed Obaid Mustafa
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Particle Swarm ◽  
Dc Motor ◽  
Weight Functions ◽  
Swarm Optimization ◽  
Acceleration Coefficient ◽  
Modified Particle Swarm Optimization ◽  
Inertia Weight ◽  
Property Control

A significant problem in the control field is the adjustment of PID controller parameters. Because of its high nonlinearity property, control of the DC motor system is difficult and mathematically repetitive. The particle swarm optimization PSO solution is a great optimization technique and a promising approach to address the problem of optimum PID controller results. In this paper, a modified particle swarm optimization PSO method with four inertia weight functions is suggested to find the global optimum parameters of the PID controller for speed and position control of the DC motor. Benchmark studies of inertia weight functions are described. Two scenarios have been suggested in order to modify PSO including the first scenario called M1-PSO and the second scenario called M2-PSO, as well as classical PSO algorithms. For the first scenario, the modification of the PSO was done based on changing the four inertia weight functions, social and personal acceleration coefficient, while in the second scenario, the four inertia weight functions have been changed but the social and personal acceleration coefficient stayed constant during the algorithm implementation. The comparison between the presented scenarios and traditional PID was carried out and satisfied simulation results have shown that the first scenario has rapid search speeds, and very effective and fast implementation compared to the second scenario and classical PSO and even improved PSO technique. Moreover, the proposed approach has a fast searching speed compared to classical PSO. However, it has been found that the classical PSO algorithm has a premature, inaccurate and local convergence process when solving complex optimization issues. The presented algorithm is proposed to increase the search speed of the original PSO.

scholarly journals A MODIFIED PARTICLE SWARM OPTIMIZATION WITH RANDOM ACTIVATION FOR INCREASING EXPLORATION

Kursor ◽  
2016 ◽  
Vol 8 (1) ◽  
pp. 33
Author(s):  
Alrijadjis Alrijadjis
Keyword(s):  
Particle Swarm Optimization ◽  
Search Algorithm ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Premature Convergence ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Inertia Weight ◽  
Modified Pso

Particle Swarm Optimization (PSO) is a popular optimization technique which is inspired by the social behavior of birds flocking or fishes schooling for finding food. It is a new metaheuristic search algorithm developed by Eberhart and Kennedy in 1995. However, the standard PSO has a shortcoming, i.e., premature convergence and easy to get stack or fall into local optimum. Inertia weight is an important parameter in PSO, which significantly affect the performance of PSO. There are many variations of inertia weight strategies have been proposed in order to overcome the shortcoming. In this paper, a new modified PSO with random activation to increase exploration ability, help trapped particles for jumping-out from local optimum and avoid premature convergence is proposed. In the proposed method, an inertia weight is decreased linearly until half of iteration, and then a random number for an inertia weight is applied until the end of iteration. To emphasis the role of this new inertia weight adjustment, the modified PSO paradigm is named Modified PSO with random activation (MPSO-RA). The experiments with three famous benchmark functions show that the accuracy and success rate of the proposed MPSO-RA increase of 43.23% and 32.95% compared with the standard PSO.

scholarly journals Optimal Configuration of Wind Farms in Radial Distribution System Using Particle Swarm Optimization Technique

2018 ◽  
Vol 7 (2) ◽  
pp. 286-293 ◽  
Author(s):  
Mahesh Kumar ◽  
Bhagwan Das ◽  
Perumal Nallagownden ◽  
Irraivan Elamvazuthi ◽  
Sadia Ali Khan
Keyword(s):  
Particle Swarm Optimization ◽  
Radial Distribution ◽  
Distribution System ◽  
Particle Swarm ◽  
Wind Farms ◽  
Optimal Location ◽  
Optimal Configuration ◽  
Wind Data ◽  
Swarm Optimization ◽  
Radial Distribution System

Recently, a wide range of wind farm based distributed generations (DGs) are being integrated into distribution systems to fulfill energy demands and to reduce the burden on transmission corridors. The non-optimal configuration of DGs could severely affect the distribution system operations and control. Hence, the aim of this paper is to analyze the wind data in order to build a mathematical model for power output and pinpoint the optimal location. The overall objective is minimization of power loss reduction in distribution system. The five years of wind data was taken from 24o 44’ 29” North, 67o 35’ 9” East coordinates in Pakistan. The optimal location for these wind farms were pinpointed via particle swarm optimization (PSO) algorithm using standard IEEE 33 radial distribution system. The result reveals that the proposed method helps in improving renewable energy near to load centers, reduce power losses and improve voltage profile of the system. Moreover, the validity and performance of the proposed model were also compared with other optimization algorithms.

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