scholarly journals Congestion Management in Deregulated Power System using Adaptive Moth Swarm Optimization

2021 ◽  
Author(s):  
R Ramaporselvi ◽  
G. Geetha
Keyword(s):  
Power System ◽  
Congestion Management ◽  
Optimal Location ◽  
Bacterial Foraging Optimization ◽  
Swarm Optimization ◽  
The Past ◽  
Deregulated Power System ◽  
Thyristor Controlled Series Capacitor ◽  
Compensation Rate ◽  
Bacterial Foraging Optimization Algorithm

Abstract 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 utilizing the available power without breaking the system hindrances or limitations. Over the past few years, determining an 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 thyristor-controlled series capacitor (TCSC) using adaptive moth swarm optimization (AMSO) algorithm. An adaptive moth swarm AMSO algorithm utilizes the performances of moth flame and chaotic local search-based shrinking scheme of the bacterial foraging optimization algorithm. The proposed AMSO approach is executed and discussed for IEEE-30 bus system for determining the optimal location of single TCSC and dual TCSC. In addition to this, the proposed algorithm is compared with various other existing approaches and the results thus obtained provide better performances when compared with other techniques.

scholarly journals Congestion management in deregulated power system using adaptive moth swarm optimization

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
R. Ramaporselvi ◽  
G. Geetha
Keyword(s):  
Power System ◽  
Power Loss ◽  
Design Methodology ◽  
Congestion Management ◽  
Optimal Location ◽  
Bacterial Foraging Optimization ◽  
Swarm Optimization ◽  
Content Type ◽  
The Past ◽  
Compensation Rate

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.

scholarly journals Saddle Node Bifurcation Point Analysis of Voltage Stability of IEEE 30-BUS Power System for Removal of Generation through Bacteria Foraging Optimization Algorithm

2018 ◽  
Vol 7 (3.31) ◽  
pp. 36
Author(s):  
Srikanth B. Venkata ◽  
Lakshmi Devi Ai
Keyword(s):  
Power System ◽  
Optimization Algorithm ◽  
Reactive Power ◽  
Reactive Power Compensation ◽  
Bacterial Foraging Optimization ◽  
Saddle Node Bifurcation ◽  
Saddle Node ◽  
Point Analysis ◽  
Bacteria Foraging Optimization Algorithm ◽  
Bacterial Foraging Optimization Algorithm

This paper deals with the identification of instability nodes of IEEE 30 BUS power system to generation removal. Optimal sizing and locations of reactive power compensations are obtained. Firstly one of the generators is assumed to be removed from service and the saddle node bifurcation (SNB) point voltages are evaluated without reactive power compensation. Secondly two generators are assumed to be removed from service and the saddle node point voltage magnitudes are obtained without reactive power compensation. For both cases the study is conducted by placing optimal reactive power compensations at optimal locations using Bacterial Foraging Optimization Algorithm (BFOA).  

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