Congestion management in deregulated power system using adaptive moth swarm optimization

Author:

Ramaporselvi R.,Geetha G.

Abstract

Purpose The purpose of this paper is to enhance the line congestion and to minimize power loss. Transmission line congestion is considered the most acute trouble during the operation of the power system. Therefore, congestion management acts as an effective tool in using the available power without breaking the system hindrances or limitations. Design/methodology/approach Over the past few years, determining the optimal location and size of the devices have pinched a great deal of consideration. Numerous approaches have been established to mitigate the congestion rate, and this paper aims to enhance the line congestion and minimize power loss by determining the compensation rate and optimal location of a thyristor-switched capacitor (TCSC) using adaptive moth swarm optimization (AMSO) algorithm. Findings An AMSO algorithm uses the performances of moth flame and the chaotic local search-based shrinking scheme of the bacterial foraging optimization algorithm. The proposed AMSO approach is executed and discussed for the IEEE-30 bus system for determining the optimal location of single TCSC and dual TCSC. Originality/value In addition to this, the proposed algorithm is compared with various other existing approaches, and the results thus obtained provide better performances than other techniques.

Publisher

Emerald

Subject

Applied Mathematics,Electrical and Electronic Engineering,Computational Theory and Mathematics,Computer Science Applications

Reference34 articles.

1. Optimal placement and sizing of multi-type FACTS devices in power systems using metaheuristic optimisation techniques: an updated review;Ain Shams Engineering Journal,2020

2. Congestion management through optimal allocation of FACTS devices using DigSILENT-based DPSO algorithm-a real case study;Journal of Operation and Automation in Power Engineering,2020

3. Semi-globally practical finite-time ${H} _ {\infty} $control of TCSC model of power systems based on dynamic surface control. (2020);IEEE Access,2020

4. An enhanced bacterial foraging optimization and its application for training kernel extreme learning machine;Applied Soft Computing,2020

5. Transmission congestion management using multi objective hybrid flower pollination and particle swarm optimization algorithm by optimal placement of TCSC,2021

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