Physics-Informed Kolmogorov-Arnold Networks for Power System Dynamics
This paper presents, for the first time, a framework for Kolmogorov-Arnold Networks (KANs) in power system applications. Inspired by the recently proposed KAN architecture, this paper proposes physics-informed Kolmogorov-Arnold Networks (PIKANs), a novel KAN-based physics-informed neural network (PI...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Open Access Journal of Power and Energy |
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| Online Access: | https://ieeexplore.ieee.org/document/10843279/ |
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| author | Hang Shuai Fangxing Li |
| author_facet | Hang Shuai Fangxing Li |
| author_sort | Hang Shuai |
| collection | DOAJ |
| description | This paper presents, for the first time, a framework for Kolmogorov-Arnold Networks (KANs) in power system applications. Inspired by the recently proposed KAN architecture, this paper proposes physics-informed Kolmogorov-Arnold Networks (PIKANs), a novel KAN-based physics-informed neural network (PINN) tailored to efficiently and accurately learn dynamics within power systems. PIKANs offer a promising alternative to conventional Multi-Layer Perceptrons (MLPs) based PINNs, achieving superior accuracy in predicting power system dynamics while employing a smaller network size. Simulation results on test power systems underscore the accuracy of the PIKANs in predicting rotor angle and frequency with fewer learnable parameters than conventional PINNs. Specifically, PIKANs can achieve higher accuracy while utilizing only 50% of the network size required by conventional PINNs. Furthermore, simulation results demonstrate PIKANs’ capability to accurately identify uncertain inertia and damping coefficients. This work opens up a range of opportunities for the application of KANs in power systems, enabling efficient dynamic analysis and precise parameter identification. |
| format | Article |
| id | doaj-art-5c149b6426944a578822cf8553c9eef7 |
| institution | Kabale University |
| issn | 2687-7910 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Access Journal of Power and Energy |
| spelling | doaj-art-5c149b6426944a578822cf8553c9eef72025-02-05T00:01:21ZengIEEEIEEE Open Access Journal of Power and Energy2687-79102025-01-0112465810.1109/OAJPE.2025.352992810843279Physics-Informed Kolmogorov-Arnold Networks for Power System DynamicsHang Shuai0https://orcid.org/0000-0001-5087-699XFangxing Li1https://orcid.org/0000-0003-1060-7618Department of Electrical Engineering and Computer Science, The University of Tennessee, Knoxville, TN, USADepartment of Electrical Engineering and Computer Science, The University of Tennessee, Knoxville, TN, USAThis paper presents, for the first time, a framework for Kolmogorov-Arnold Networks (KANs) in power system applications. Inspired by the recently proposed KAN architecture, this paper proposes physics-informed Kolmogorov-Arnold Networks (PIKANs), a novel KAN-based physics-informed neural network (PINN) tailored to efficiently and accurately learn dynamics within power systems. PIKANs offer a promising alternative to conventional Multi-Layer Perceptrons (MLPs) based PINNs, achieving superior accuracy in predicting power system dynamics while employing a smaller network size. Simulation results on test power systems underscore the accuracy of the PIKANs in predicting rotor angle and frequency with fewer learnable parameters than conventional PINNs. Specifically, PIKANs can achieve higher accuracy while utilizing only 50% of the network size required by conventional PINNs. Furthermore, simulation results demonstrate PIKANs’ capability to accurately identify uncertain inertia and damping coefficients. This work opens up a range of opportunities for the application of KANs in power systems, enabling efficient dynamic analysis and precise parameter identification.https://ieeexplore.ieee.org/document/10843279/Kolmogorov-Arnold Networks (KANs)power system dynamicsdeep learningswing equationphysics-informed neural network (PINN) |
| spellingShingle | Hang Shuai Fangxing Li Physics-Informed Kolmogorov-Arnold Networks for Power System Dynamics IEEE Open Access Journal of Power and Energy Kolmogorov-Arnold Networks (KANs) power system dynamics deep learning swing equation physics-informed neural network (PINN) |
| title | Physics-Informed Kolmogorov-Arnold Networks for Power System Dynamics |
| title_full | Physics-Informed Kolmogorov-Arnold Networks for Power System Dynamics |
| title_fullStr | Physics-Informed Kolmogorov-Arnold Networks for Power System Dynamics |
| title_full_unstemmed | Physics-Informed Kolmogorov-Arnold Networks for Power System Dynamics |
| title_short | Physics-Informed Kolmogorov-Arnold Networks for Power System Dynamics |
| title_sort | physics informed kolmogorov arnold networks for power system dynamics |
| topic | Kolmogorov-Arnold Networks (KANs) power system dynamics deep learning swing equation physics-informed neural network (PINN) |
| url | https://ieeexplore.ieee.org/document/10843279/ |
| work_keys_str_mv | AT hangshuai physicsinformedkolmogorovarnoldnetworksforpowersystemdynamics AT fangxingli physicsinformedkolmogorovarnoldnetworksforpowersystemdynamics |