Lion algorithm for generation rescheduling based congestion management in deregulated power system

Paraskar, Sudhir R.; Singh, Dharmendra Kumar; Tapre, Pawan C.

doi:10.1109/icecds.2017.8390195

Cited by 3 publications

(2 citation statements)

References 30 publications

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“…The cost-free technique comprises topology network modification, transformer tap installation and the functioning of various compensating devices, namely, flexible AC transmission system (FACTS), phase shifters, etc. The non-cost-free technique is accomplished by the use of curtailment of load transactions, load curtailment and generator rescheduling (Paraskar et al , 2017).…”

Section: Introductionmentioning

confidence: 99%

Congestion management in deregulated power system using adaptive moth swarm optimization

Ramaporselvi¹,

Geetha²

2021

COMPEL

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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.

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Section: Introductionmentioning

confidence: 99%

Congestion management in deregulated power system using adaptive moth swarm optimization

Ramaporselvi¹,

Geetha²

2021

COMPEL

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“…The cost-free technique comprises topology network modification, transformer tap installation as well as the functioning of various compensating devices namely flexible AC transmission system (FACTS), phase shifters, etc. The non-cost free technique is accomplished by the use of curtailment of load transactions, load curtailment as well as generator rescheduling [3].…”

Section: Introductionmentioning

confidence: 99%

Congestion Management in Deregulated Power System using Adaptive Moth Swarm Optimization

Ramaporselvi

Geetha

2021

Preprint

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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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Review of Metaheuristics Inspired from the Animal Kingdom

Drăgoi

Dafinescu

2021

Mathematics

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The search for powerful optimizers has led to the development of a multitude of metaheuristic algorithms inspired from all areas. This work focuses on the animal kingdom as a source of inspiration and performs an extensive, yet not exhaustive, review of the animal inspired metaheuristics proposed in the 2006–2021 period. The review is organized considering the biological classification of living things, with a breakdown of the simulated behavior mechanisms. The centralized data indicated that 61.6% of the animal-based algorithms are inspired from vertebrates and 38.4% from invertebrates. In addition, an analysis of the mechanisms used to ensure diversity was performed. The results obtained showed that the most frequently used mechanisms belong to the niching category.

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Lion algorithm for generation rescheduling based congestion management in deregulated power system

Cited by 3 publications

References 30 publications

Congestion management in deregulated power system using adaptive moth swarm optimization

Congestion management in deregulated power system using adaptive moth swarm optimization

Congestion Management in Deregulated Power System using Adaptive Moth Swarm Optimization

Review of Metaheuristics Inspired from the Animal Kingdom

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