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Application of Moth-Flame Optimization Algorithm for the Determination of Maximum Loading Limit of Power System: Application of MFO for Maximum Loading Limit

Application of Moth-Flame Optimization Algorithm for the Determination of Maximum Loading Limit of Power System: Application of MFO for Maximum Loading Limit

Suvabrata Mukherjee, Provas Kumar Roy
Copyright: © 2020 |Pages: 16
ISBN13: 9781522585510|ISBN10: 1522585516|ISBN13 Softcover: 9781522585527|EISBN13: 9781522585534
DOI: 10.4018/978-1-5225-8551-0.ch003
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MLA

Mukherjee, Suvabrata, and Provas Kumar Roy. "Application of Moth-Flame Optimization Algorithm for the Determination of Maximum Loading Limit of Power System: Application of MFO for Maximum Loading Limit." Novel Advancements in Electrical Power Planning and Performance, edited by Smita Shandilya, et al., IGI Global, 2020, pp. 78-93. https://doi.org/10.4018/978-1-5225-8551-0.ch003

APA

Mukherjee, S. & Roy, P. K. (2020). Application of Moth-Flame Optimization Algorithm for the Determination of Maximum Loading Limit of Power System: Application of MFO for Maximum Loading Limit. In S. Shandilya, S. Shandilya, T. Thakur, & A. Nagar (Eds.), Novel Advancements in Electrical Power Planning and Performance (pp. 78-93). IGI Global. https://doi.org/10.4018/978-1-5225-8551-0.ch003

Chicago

Mukherjee, Suvabrata, and Provas Kumar Roy. "Application of Moth-Flame Optimization Algorithm for the Determination of Maximum Loading Limit of Power System: Application of MFO for Maximum Loading Limit." In Novel Advancements in Electrical Power Planning and Performance, edited by Smita Shandilya, et al., 78-93. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-5225-8551-0.ch003

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Abstract

Moth-flame optimization algorithm (MFOA) based on the navigation strategy of moths in universe is a novel bio-inspired optimization technique and has been exerted for determining the maximum loading limit of power system. This process is highly effective for traversing long distances following a straight path. As a matter of fact, moths follow a deadly spiral path as artificial lights tend to confuse them. Exploration and exploitation are two vital aspects of the algorithm, used in tuning of the parameters. The algorithm is verified on MATPOWER case30 and case118 systems. Comparison of the performance of MFOA has been done with other evolutionary algorithms such as multi-agent hybrid PSO (MAHPSO), differential evolution (DA), hybridized DE, and PSO (DEPSO). The performance of MFOA in determining maximum loading limit is verified from the results. In much reduced time, MFO algorithm also gives high maximum loading point (MLP).

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