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A Brief Review and Comparative Study of Nature-Inspired Optimization Algorithms Applied to Power System Control

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Book cover Natural Computing for Unsupervised Learning

Part of the book series: Unsupervised and Semi-Supervised Learning ((UNSESUL))

Abstract

This work deals with the use of a special class of optimization algorithms called nature-inspired optimization algorithms (NIOA) to improve power system control actions. This work discusses also the optimization issue of the control task in power system. As an example of nature-inspired (NI) algorithm, various swarm intelligence (SI) and bio-inspired (BI) algorithms that mimic the social, living, and hunting behavior of many kinds of animal, insects, and creatures in nature such as wolves, elephants, whale, fishes, spider, bees, ants, bats, and birds were used as an optimization tool. The main aim was to enhance frequency and voltage regulation loops to cope with system fluctuations during disturbances. The purpose was to optimize the Power System Stabilizer (PSS) parameters and the PID controller gains for enhancing both load frequency control (LFC) and automatic voltage regulator (AVR) systems. To satisfy the objective of this work, a series of simulations on single-area power system with standard LFC and AVR loops was performed. To show the contribution of each applied method, a comparative study in view of peak overshoot, peak undershoot, and settling time was carried out.

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Author information

Authors and Affiliations

  1. Laboratory of Electrical and Industrial Systems, Faculty of Electrical Engineering and Computing, University of Sciences and Technology Houari Boumediene, Algiers, Algeria

    Nour E. L. Yakine Kouba & Mohamed Boudour

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  1. Nour E. L. Yakine Kouba
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  2. Mohamed Boudour
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Editors and Affiliations

  1. Department of Information Sciences and Technology, Northeast Normal University, Changchun, Jilin, China

    Xiangtao Li

  2. City University of Hong Kong, Kowloon Tong, Hong Kong

    Ka-Chun Wong

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Kouba, N.E.L.Y., Boudour, M. (2019). A Brief Review and Comparative Study of Nature-Inspired Optimization Algorithms Applied to Power System Control. In: Li, X., Wong, KC. (eds) Natural Computing for Unsupervised Learning. Unsupervised and Semi-Supervised Learning. Springer, Cham. https://doi.org/10.1007/978-3-319-98566-4_2

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  • DOI: https://doi.org/10.1007/978-3-319-98566-4_2

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