Quantum Neural Networks for Solving Power System Transient Simulation Problem

Mohammadreza Soltaninia and Junpeng Zhan ()
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Mohammadreza Soltaninia: Department of Electrical Engineering, Alfred University, Alfred, NY 14802, USA
Junpeng Zhan: Department of Electrical Engineering, Alfred University, Alfred, NY 14802, USA

Energies, 2025, vol. 18, issue 10, 1-20

Abstract: Quantum computing, leveraging principles of quantum mechanics, represents a transformative approach in computational methodologies, offering significant enhancements over traditional classical systems. This study tackles the complex and computationally demanding task of simulating power system transients through solving differential-algebraic equations (DAEs). We introduce two novel Quantum Neural Networks (QNNs): the Sinusoidal-Friendly QNN and the Polynomial-Friendly QNN, proposing them as effective alternatives to conventional simulation techniques. Our application of these QNNs successfully simulates two small power systems, demonstrating their potential to achieve good accuracy. We further explore various configurations, including time intervals, training points, and the selection of classical optimizers, to optimize the solving of DAEs using QNNs. This research not only marks a pioneering effort in applying quantum computing to power system simulations but also expands the potential of quantum technologies in addressing intricate engineering challenges.

Keywords: differential-algebraic equations; power system transient simulation; quantum neural network (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: 2025
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