IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v318y2025ics0360544225005730.html
   My bibliography  Save this article

Golden eagle optimization algorithm embedded in gated Kolmogorov-Arnold network for transient stability preventive control of power systems

Author

Listed:
  • Yin, Linfei
  • Ge, Wei
  • Liu, Rongkun

Abstract

With the expansion of the power grid-scale and intelligent development, the investigation of transient stability preventive control (TSPC) is becoming more and more essential. The traditional TSPC contains the solution of nonlinear differential-algebraic equations (NDEs), which is a complex and time-consuming computational process that fails to meet the real-time requirements of TSPC. Therefore, this study combines the gated cyclic unit with the Kolmogorov-Arnold network (KAN) and proposes a transient stability prediction (TSP) model based on the gated Kolmogorov-Arnold network (GKAN) instead of the NDEs and combines this model with the golden eagle optimization (GEO) algorithm for the TSPC. The GKAN-based TSP model and GEO are compared and experimented on two systems, IEEE 39–46 system and IEEE 145–453 system, respectively. The experimental results show that in the IEEE 39–46 system, the root mean square error, mean absolute error, and mean absolute percentage error of the GKAN-based TSP model are 53.03 %, 70.67 %, and 66.00 % lower than the suboptimal model, respectively, and the coefficient of determination is 1.30 % higher, and the value of the objective function of GEO is 4.05 % lower than that of the suboptimal algorithm; for the IEEE 145–453 system, the corresponding values are 56.59 %, 80.96 %, 77.12 %, 0.76 % and 4.30 %, respectively.

Suggested Citation

  • Yin, Linfei & Ge, Wei & Liu, Rongkun, 2025. "Golden eagle optimization algorithm embedded in gated Kolmogorov-Arnold network for transient stability preventive control of power systems," Energy, Elsevier, vol. 318(C).
    • Handle: RePEc:eee:energy:v:318:y:2025:i:c:s0360544225005730
    DOI: 10.1016/j.energy.2025.134931
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544225005730
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2025.134931?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Gao, Yuan & Hu, Zehuan & Chen, Wei-An & Liu, Mingzhe & Ruan, Yingjun, 2025. "A revolutionary neural network architecture with interpretability and flexibility based on Kolmogorov–Arnold for solar radiation and temperature forecasting," Applied Energy, Elsevier, vol. 378(PA).
    2. Chen, Yunxiao & Lin, Chaojing & Zhang, Yilan & Liu, Jinfu & Yu, Daren, 2024. "Proactive failure warning for wind power forecast models based on volatility indicators analysis," Energy, Elsevier, vol. 305(C).
    3. Miao, Cairan & Wang, Qi & Tang, Yi, 2023. "A gas-thermal inertia-based frequency response strategy considering the suppression of a second frequency dip in an integrated energy system," Energy, Elsevier, vol. 263(PD).
    4. Ifaei, Pouya & Nazari-Heris, Morteza & Tayerani Charmchi, Amir Saman & Asadi, Somayeh & Yoo, ChangKyoo, 2023. "Sustainable energies and machine learning: An organized review of recent applications and challenges," Energy, Elsevier, vol. 266(C).
    5. Luo, Xing & Zhang, Dongxiao, 2023. "A cascaded deep learning framework for photovoltaic power forecasting with multi-fidelity inputs," Energy, Elsevier, vol. 268(C).
    6. Quan, Hao & Wang, Wenyu & Zhang, Shaojia & Zou, Yun, 2024. "Probabilistic assessment method of small-signal stability in power systems based on quantitative PSS analysis," Applied Energy, Elsevier, vol. 375(C).
    7. Shaheen, Mohamed A.M. & Ullah, Zia & Hasanien, Hany M. & Tostado-Véliz, Marcos & Ji, Haoran & Qais, Mohammed H. & Alghuwainem, Saad & Jurado, Francisco, 2023. "Enhanced transient search optimization algorithm-based optimal reactive power dispatch including electric vehicles," Energy, Elsevier, vol. 277(C).
    8. Xue, Jingsong & Ma, Wentao & Feng, Xiaoyang & Guo, Peng & Guo, Yaosong & Hu, Xianzhi & Chen, Badong, 2023. "Stacking integrated learning model via ELM and GRU with mixture correntropy loss for robust state of health estimation of lithium-ion batteries," Energy, Elsevier, vol. 284(C).
    9. Sulaiman, Mohd Herwan & Mustaffa, Zuriani & Mohamed, Amir Izzani & Samsudin, Ahmad Salihin & Mohd Rashid, Muhammad Ikram, 2024. "Battery state of charge estimation for electric vehicle using Kolmogorov-Arnold networks," Energy, Elsevier, vol. 311(C).
    10. Zhou, Dengji & Jia, Xingyun & Ma, Shixi & Shao, Tiemin & Huang, Dawen & Hao, Jiarui & Li, Taotao, 2022. "Dynamic simulation of natural gas pipeline network based on interpretable machine learning model," Energy, Elsevier, vol. 253(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lu, Yu & Xiang, Yue & Huang, Yuan & Yu, Bin & Weng, Liguo & Liu, Junyong, 2023. "Deep reinforcement learning based optimal scheduling of active distribution system considering distributed generation, energy storage and flexible load," Energy, Elsevier, vol. 271(C).
    2. Tao, Kejun & Zhao, Jinghao & Tao, Ye & Qi, Qingqing & Tian, Yajun, 2024. "Operational day-ahead photovoltaic power forecasting based on transformer variant," Applied Energy, Elsevier, vol. 373(C).
    3. Guo, Siyi & Wei, Ziqing & Yin, Yaling & Zhai, Xiaoqiang, 2025. "A physics-guided RNN-KAN for multi-step prediction of heat pump operation states," Energy, Elsevier, vol. 320(C).
    4. Gong, Ying & Wang, Yongzheng & Xie, Yuanhang & Peng, Xuzhang & Peng, Yan & Zhang, Wenhua, 2025. "Dynamic fusion LSTM-Transformer for prediction in energy harvesting from human motions," Energy, Elsevier, vol. 327(C).
    5. Tian, Xingtao & Lin, Xiaojie & Zhong, Wei & Zhou, Yi, 2023. "Analytical sensitivity analysis of radial natural gas networks," Energy, Elsevier, vol. 263(PC).
    6. Yin, Linfei & Ge, Wei, 2024. "Mobileception-ResNet for transient stability prediction of novel power systems," Energy, Elsevier, vol. 309(C).
    7. Chen, Xingyuan & Hu, Yang & Zhao, Jingwei & Wang, Yini, 2025. "Downscaling deconstruction, hybrid semi-mechanism state estimation and cascaded dynamic equivalent modelling of complex district heating networks," Energy, Elsevier, vol. 322(C).
    8. Zhou, Xinlei & Du, Han & Xue, Shan & Ma, Zhenjun, 2024. "Recent advances in data mining and machine learning for enhanced building energy management," Energy, Elsevier, vol. 307(C).
    9. Gong, Junhua & Shi, Guoyun & Wang, Shaobo & Wang, Peng & Chen, Bin & Chen, Yujie & Wang, Bohong & Yu, Bo & Jiang, Weixin & Li, Zongze, 2024. "Efficient super-resolution of pipeline transient process modeling using the Fourier Neural Operator," Energy, Elsevier, vol. 302(C).
    10. Giovane Ronei Sylvestrin & Joylan Nunes Maciel & Marcio Luís Munhoz Amorim & João Paulo Carmo & José A. Afonso & Sérgio F. Lopes & Oswaldo Hideo Ando Junior, 2025. "State of the Art in Electric Batteries’ State-of-Health (SoH) Estimation with Machine Learning: A Review," Energies, MDPI, vol. 18(3), pages 1-77, February.
    11. Zhang, Yagang & Pan, Zhiya & Wang, Hui & Wang, Jingchao & Zhao, Zheng & Wang, Fei, 2023. "Achieving wind power and photovoltaic power prediction: An intelligent prediction system based on a deep learning approach," Energy, Elsevier, vol. 283(C).
    12. Zeng, Jiawei & Wang, Shunli & Cao, Wen & Zhou, Yifei & Fernandez, Carlos & Guerrero, Josep M., 2024. "Battery asynchronous fractional-order thermoelectric coupling modeling and state of charge estimation based on frequency characteristic separation at low temperatures," Energy, Elsevier, vol. 307(C).
    13. Koo, Bonchan & Chang, Seungjoon & Kwon, Hweeung, 2023. "Digital twin for natural gas infrastructure operation and management via streaming dynamic mode decomposition with control," Energy, Elsevier, vol. 274(C).
    14. Abdelfattah, Wael & Abdelhamid, Ahmed Sayed & Hasanien, Hany M. & Rashad, Basem Abd-Elhamed, 2024. "Smart vehicle-to-grid integration strategy for enhancing distribution system performance and electric vehicle profitability," Energy, Elsevier, vol. 302(C).
    15. Wang, Shuaibing & Lin, Haitao & Abed, Azher M. & Mahariq, Ibrahim & Ayed, Hamdi & Mouldi, Abir & Lin, Zhixiang, 2024. "Life cycle analysis of biowaste-to- biogas/biomethane processes: Cost and environmental assessment of four different biowaste scenarios organic fraction of municipal solid waste and secondary sewage s," Energy, Elsevier, vol. 308(C).
    16. Nassabeh, Mehdi & You, Zhenjiang & Keshavarz, Alireza & Iglauer, Stefan, 2024. "Sub-surface geospatial intelligence in carbon capture, utilization and storage: A machine learning approach for offshore storage site selection," Energy, Elsevier, vol. 305(C).
    17. Chen, Xiaodong & Ge, Xinxin & Sun, Rongfu & Wang, Fei & Mi, Zengqiang, 2024. "A SVM based demand response capacity prediction model considering internal factors under composite program," Energy, Elsevier, vol. 300(C).
    18. Huang, Congzhi & Yang, Mengyuan, 2023. "Memory long and short term time series network for ultra-short-term photovoltaic power forecasting," Energy, Elsevier, vol. 279(C).
    19. Elif Üstündağlı Erten & Iman Janghorban Esfahani & Pouya Ifaei & Ebru Belkıs Güzeloğlu, 2023. "Dwelling, Habits, and Possessions: Clustering Turkey’s Household Energy Choices through Responsible Consumption and Poverty," Energies, MDPI, vol. 16(24), pages 1-20, December.
    20. Chankook Park, 2025. "Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector," Sustainability, MDPI, vol. 17(13), pages 1-17, June.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:318:y:2025:i:c:s0360544225005730. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.