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Design and Maintenance Optimisation of Substation Automation Systems: A Multiobjectivisation Approach Exploration.

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Titel: Design and Maintenance Optimisation of Substation Automation Systems: A Multiobjectivisation Approach Exploration.
Autoren: Cacereño, Andrés, Greiner, David, Zuñiga, Andrés, Galván, Blas J.
Quelle: Journal of Engineering (2314-4912); 1/6/2024, p1-19, 19p
Schlagwörter: DISCRETE event simulation, OPTIMIZATION algorithms, INFRASTRUCTURE (Economics), GENETIC algorithms, AUTOMATION
Abstract: Substation automation systems (SAS) are critical infrastructures whose design and maintenance must be optimised to guarantee a suitable performance. In order to provide a collection of solutions that balance availability and cost, this paper explores the optimisation of the design and maintenance of a section of SAS. Multiobjective evolutionary algorithms are combined with discrete event simulation while the performance of two state-of-the-art multiobjective evolutionary algorithms is studied. On the one hand, the nondominated sorting genetic algorithm II (NSGA-II), and on the other hand, the S-metric selection evolutionary multiobjective optimisation algorithm (SMS-EMOA). Such a problem is solved from 2 and 3-objective approaches by attending to the multiobjectivisation concept. The robustness of the methodology is brought to light, and benefits were observed from the multiobjectivisation approach. Decision-makers can employ this knowledge to make informed decisions based on economic and reliability criteria. [ABSTRACT FROM AUTHOR]
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Beschreibung
Abstract:Substation automation systems (SAS) are critical infrastructures whose design and maintenance must be optimised to guarantee a suitable performance. In order to provide a collection of solutions that balance availability and cost, this paper explores the optimisation of the design and maintenance of a section of SAS. Multiobjective evolutionary algorithms are combined with discrete event simulation while the performance of two state-of-the-art multiobjective evolutionary algorithms is studied. On the one hand, the nondominated sorting genetic algorithm II (NSGA-II), and on the other hand, the S-metric selection evolutionary multiobjective optimisation algorithm (SMS-EMOA). Such a problem is solved from 2 and 3-objective approaches by attending to the multiobjectivisation concept. The robustness of the methodology is brought to light, and benefits were observed from the multiobjectivisation approach. Decision-makers can employ this knowledge to make informed decisions based on economic and reliability criteria. [ABSTRACT FROM AUTHOR]
ISSN:23144904
DOI:10.1155/2024/9390545