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Optimizing TCSC configuration via genetic algorithm for ATC enhancement

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Abstract

Flexible AC transmission systems (FACTS) are a viable way to improve power system performance because of the rapid advancement of power electronic technology. Improvement of dynamic and transient stability, voltage control, voltage stability improvement, power factor correction, enhancement of `power profile, an increase in the transmission line’s power transfer capabilities, and loss alleviation are all FACTS device applications in power system networks. But, optimal placement and sizing of the FACTS devices may cause the system to attain the objective at a reasonable cost. Hence, to find out the optimal location and compensation level of the FACTS device, an improved version of the Genetic algorithm (GA) had been introduced in the preceding version of this paper. Genetic Algorithm with Dual Mutation Probability (GADMP) was the term given to the improved version of GA since it included dual probabilities for the mutation operator. To ensure a fair comparison between the algorithms on FACTS localization and sizing, this paper subjects the GADMP and Traditional GA (TGA) to rigorous experimental investigation. Simulation experiments have been performed using three benchmark bus systems, IEEE 24 RTS, IEEE 30, and IEEE 57 to verify the performance of the adopted scheme.

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Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Abbreviations

ATC:

Avaliable Transfer Capability

ALC-PSO:

Particle Swarm Optimization with an Aging Leader and Challengers

FACTS:

Flexible Alternating Current Transmission Systems

PSO:

Particle Swarm Optimization

GA:

Genetic Algorithm

HMPSO:

Hybrid Whale-Particle Swarm Optimization

TTC:

Total Transfer Capability

GADMP:

Genetic Algorithm with Dual Mutation Probability

OASIS:

Open Access Same Time Information System

TCSC:

Thyristor Controlled Series Capacitor

RBF:

Radial Basis Function

BPA :

Back Propagation Algorithm

RTS:

Reliability Test System

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Acknowledgments

I would like to express my very great appreciation to the co-authors of this manuscript for their valuable and constructive suggestions during the planning and development of this research work.

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  1. Department of Electrical Engineering, Faculty of Engineering and Technology, Deenbandhu Chhotu Ram University of Science & Technology, Murthal, Sonepat, Haryana, India

    Naresh Kumar Yadav

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Correspondence to Naresh Kumar Yadav.

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Yadav, N.K. Optimizing TCSC configuration via genetic algorithm for ATC enhancement. Multimed Tools Appl 82, 38715–38741 (2023). https://doi.org/10.1007/s11042-023-15043-3

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