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

Josip Tosic, Srdjan Skok, Ljupko Teklic and Mislav Balkovic
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Josip Tosic: Toska Ltd., 10000 Zagreb, Croatia
Srdjan Skok: Department of Electrical Engineering, Algebra University College, 10000 Zagreb, Croatia
Ljupko Teklic: Croatian Transmission System Operator, 10000 Zagreb, Croatia
Mislav Balkovic: Department of Electrical Engineering, Algebra University College, 10000 Zagreb, Croatia

Energies, 2022, vol. 15, issue 13, 1-16

Abstract: 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.

Keywords: transmission power system optimization; transmission system restoration; artificial intelligence; artificial neural networks; power system analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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