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.

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.

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