Congestion management in deregulated power system using adaptive moth swarm optimization

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.

Inventory Management Optimization of Green Supply Chain Using IPSO-BPNN Algorithm under the Artificial Intelligence

“…Ramaporselvi and Geetha proposed an adaptive MSA optimization algorithm and applied it to the congestion management for a power system transmission line. Experimental results show that the algorithm's performance is better than the existing technology [29]. Mehta et al applied HGS to automotive engineering design and optimization.…”

Research on electrical load distribution using an improved bacterial foraging algorithm

“…Bacterial foraging algorithms are widely used, such as image segmentation, path planning, power system, parameter optimization and identification. The improvement of bacterial foraging in the existing research mainly focuses on the chemotaxis and dispersion of bacteria, as shown in the reference (Hu et al, 2020), (Chen et al, 2017), (Wang et al, 2019), (Ramaporselvi and Geetha 2021) and (Devi and Srinivasan 2021). The contributions of this paper mainly focus on the improvement of the three key steps of the algorithm.…”