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Resilience Neural-Network-Based Methodology Applied on Optimized Transmission Systems Restoration (CROSBI ID 313115)

Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija

Tošić, Josip ; Skok, Srdjan ; Teklić, Ljupko ; Balković, Mislav Resilience Neural-Network-Based Methodology Applied on Optimized Transmission Systems Restoration // Energies (Basel), 15 (2022), 4694, 16. doi: 10.3390/en15134694

Podaci o odgovornosti

Tošić, Josip ; Skok, Srdjan ; Teklić, Ljupko ; Balković, Mislav

engleski

Resilience Neural-Network-Based Methodology Applied on Optimized Transmission Systems Restoration

This paper presents an advanced methodology for restoration of the electric power transmission system after its partial or complete failure. This load-optimized restoration is dependent on sectioning of the transmission system based on artificial neural networks. The proposed methodology and the underlying algorithm consider the transmission system operation state just before the fallout and, based on this state, calculate the power grid parameters and suggest the methodology for system restoration for each individual interconnection area. The novel methodology proposes an optimization objective function as a maximum load recovery under a set of constraints. The grid is analyzed using a large amount of data, which results in an adequate number of training data for artificial neural networks. Once the artificial neural network is trained, it provides an almost instantaneous network recovery plan scheme by defining the direct switching order.

transmission power system optimization ; transmission system restoration ; artificial intelligence ; artificial neural networks ; power system analysis

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Podaci o izdanju

15

2022.

4694

16

objavljeno

1996-1073

10.3390/en15134694

Povezanost rada

Elektrotehnika, Informacijske i komunikacijske znanosti, Računarstvo

Poveznice
Indeksiranost