Solving Economic Load Dispatch Problem UsingParticle Swarm Optimization Technique

Aditya Tiwari Et.Al

doi:10.17762/turcomat.v12i3.1565

Abstract

Economic load dispatch (ELD)is one of the important problems ofpower system operation. Conventional methods like Lambda iteration methodare not efficientfor complex ELD problems. Particle swarm optimization is preferred in ELD problem due to its high performance.The Inertia Weight PSO and Constriction Factor PSO algorithms are performed on threeunit and sixunit systems. The analysis of ELD problem is performed by Conventional method and PSO method. In this paper,losses are neglected in the ELD problem. PSO algorithm obtains the best solution forELD problem.

Highlights

The economic load dispatch problemisone of theoptimization problems
The conventional method cannot obtain the best solution tothe Economic load dispatch (ELD) problem.PSO is the most efficient methodfor economic load dispatch problem.Various PSO algorithms used in this paper are Inertia weight PSO (IPSO) and Constriction factor PSO
The PSO algorithm offers the best solution for optimization problems

Summary

Introduction

The economic load dispatch problemisone of theoptimization problems. The mainaim of thisproblem is to minimize the total cost of generation.The conventional methods require more computation time in the ELD problem. PSO and Genetic Algorithm methodsare mainly used in ELD problems. The conventional method cannot obtain the best solution tothe ELD problem.PSO is the most efficient methodfor economic load dispatch problem.Various PSO algorithms used in this paper are Inertia weight PSO (IPSO) and Constriction factor PSO (CPSO) algorithm[1]. Various optimization problems are not solved by the single optimization. Different optimization methods are available for various optimization problems. The PSO algorithm offers the best solution for optimization problems. Modern optimization methods like PSO areeffective for engineering problems. The main aspect of PSO algorithm is its simplicity and a relatively less number of parameters

Problem Formulation

Particle Swarm Optimization

Inertia weight Particle Swarm Optimization

Constriction factor Particle swarm optimization

Case Study 1: 3 UNIT SYSTEM

CONCLUSION