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Soft Computing

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04.10.2021 | Application of soft computing

Multiple-deme parallel genetic algorithm based on modular neural network for effective load shedding

verfasst von: Ali Gholami-Rahimabadi, Hadi Razmi, Hasan Doagou-Mojarrad

Erschienen in: Soft Computing | Ausgabe 21/2021

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Abstract

One of the most effective corrective control strategies to prevent voltage collapse and instability is load shedding. In this paper, a multiple-deme parallel genetic algorithm is used for a suitable design of load shedding. The load shedding algorithm is implemented when the voltage stability margin index of the power system is lower than a predefined value. In order to increase the computational speed, the voltage stability margin index is estimated by a modular neural network method in a fraction of a second. In addition, in order to use the exact values of the voltage stability margin index for neural network training, a simultaneous equilibrium tracing technique has been employed considering the detailed model of the components of the generating units such as the governor and the excitation system. In the proposed algorithm, the entire population is partitioned into several isolated subpopulations (demes) in which demes distributed in different processors and individuals may migrate occasionally from one subpopulation to another. The proposed technique has been tested on New England-39 bus test system, and the obtained results indicate the efficiency of the proposed method.

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Titel: Multiple-deme parallel genetic algorithm based on modular neural network for effective load shedding
verfasst von: Ali Gholami-Rahimabadi
Hadi Razmi
Hasan Doagou-Mojarrad
Publikationsdatum: 04.10.2021
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 21/2021
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-021-06186-2

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