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Quantum Neural Networks for Solving Power System Transient Simulation Problem.

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Title: Quantum Neural Networks for Solving Power System Transient Simulation Problem.
Authors: Soltaninia, Mohammadreza1 (AUTHOR), Zhan, Junpeng1 (AUTHOR) zhanj@alfred.edu
Source: Energies (19961073). May2025, Vol. 18 Issue 10, p2525. 19p.
Subjects: Power system simulation, Differential-algebraic equations, Quantum computing, Quantum mechanics, Computer systems
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. [ABSTRACT FROM AUTHOR]
Copyright of Energies (19961073) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Quantum Neural Networks for Solving Power System Transient Simulation Problem.
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  Data: <searchLink fieldCode="AR" term="%22Soltaninia%2C+Mohammadreza%22">Soltaninia, Mohammadreza</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhan%2C+Junpeng%22">Zhan, Junpeng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> zhanj@alfred.edu</i>
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  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. May2025, Vol. 18 Issue 10, p2525. 19p.
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  Data: <searchLink fieldCode="DE" term="%22Power+system+simulation%22">Power system simulation</searchLink><br /><searchLink fieldCode="DE" term="%22Differential-algebraic+equations%22">Differential-algebraic equations</searchLink><br /><searchLink fieldCode="DE" term="%22Quantum+computing%22">Quantum computing</searchLink><br /><searchLink fieldCode="DE" term="%22Quantum+mechanics%22">Quantum mechanics</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+systems%22">Computer systems</searchLink>
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  Data: 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Energies (19961073) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – SubjectFull: Differential-algebraic equations
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      – SubjectFull: Quantum computing
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