Quantum Neural Networks for Solving Power System Transient Simulation Problem
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 trans...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/10/2525 |
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| _version_ | 1849327618357198848 |
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| author | Mohammadreza Soltaninia Junpeng Zhan |
| author_facet | Mohammadreza Soltaninia Junpeng Zhan |
| author_sort | Mohammadreza Soltaninia |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-16db125bf81d44cab826e90da6937f08 |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-16db125bf81d44cab826e90da6937f082025-08-20T03:47:49ZengMDPI AGEnergies1996-10732025-05-011810252510.3390/en18102525Quantum Neural Networks for Solving Power System Transient Simulation ProblemMohammadreza Soltaninia0Junpeng Zhan1Department of Electrical Engineering, Alfred University, Alfred, NY 14802, USADepartment of Electrical Engineering, Alfred University, Alfred, NY 14802, USAQuantum 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.https://www.mdpi.com/1996-1073/18/10/2525differential-algebraic equationspower system transient simulationquantum neural network |
| spellingShingle | Mohammadreza Soltaninia Junpeng Zhan Quantum Neural Networks for Solving Power System Transient Simulation Problem Energies differential-algebraic equations power system transient simulation quantum neural network |
| title | Quantum Neural Networks for Solving Power System Transient Simulation Problem |
| title_full | Quantum Neural Networks for Solving Power System Transient Simulation Problem |
| title_fullStr | Quantum Neural Networks for Solving Power System Transient Simulation Problem |
| title_full_unstemmed | Quantum Neural Networks for Solving Power System Transient Simulation Problem |
| title_short | Quantum Neural Networks for Solving Power System Transient Simulation Problem |
| title_sort | quantum neural networks for solving power system transient simulation problem |
| topic | differential-algebraic equations power system transient simulation quantum neural network |
| url | https://www.mdpi.com/1996-1073/18/10/2525 |
| work_keys_str_mv | AT mohammadrezasoltaninia quantumneuralnetworksforsolvingpowersystemtransientsimulationproblem AT junpengzhan quantumneuralnetworksforsolvingpowersystemtransientsimulationproblem |