IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v309y2024ics0360544224029384.html

Mobileception-ResNet for transient stability prediction of novel power systems

Author

Listed:
  • Yin, Linfei
  • Ge, Wei

Abstract

Power system transient stability prediction (TSP) is particularly important as power systems change and evolve, including the rapid growth of renewable energy, the proliferation of electric vehicles, and the construction of smart grids. Traditional time-domain simulation methods are time-consuming and cannot achieve online prediction. Direct methods are poorly adapted and cannot be applied to complex power systems. Existing machine learning algorithms only classify the transient stability without providing the degree of transient stability of the system. Therefore, a fast and accurate power system TSP method is needed to assist operators in implementing timely measures to improve the stability of the power system running. This study proposes a Mobileception-ResNet network, Mobileception-ResNet is formed by Inception-ResNet-v2, MobileNet-v2, and a fully connected layer. In this study, Mobileception-ResNet and nine comparison models are experimented on two node systems, i.e., the IEEE 10–39 and 69–300 systems. In the IEEE 10–39 system, the root mean square error, mean absolute error, and mean absolute percentage error of Mobileception-ResNet are 44.13 %, 36.74 %, and 39.96 % lower, and the coefficient of determination is 0.04 % higher, respectively, when compared to the comparative model with the best evaluation indicator; in the IEEE 69–300 system, the corresponding values are 2.6 %, 12.83 %, 12.55 %, and 0.01 %, respectively.

Suggested Citation

  • Yin, Linfei & Ge, Wei, 2024. "Mobileception-ResNet for transient stability prediction of novel power systems," Energy, Elsevier, vol. 309(C).
    • Handle: RePEc:eee:energy:v:309:y:2024:i:c:s0360544224029384
    DOI: 10.1016/j.energy.2024.133163
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544224029384
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2024.133163?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. Pan, Zhenning & Yu, Tao & Li, Jie & Qu, Kaiping & Yang, Bo, 2020. "Risk-averse real-time dispatch of integrated electricity and heat system using a modified approximate dynamic programming approach," Energy, Elsevier, vol. 198(C).
    2. 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).
    3. Zhan, Xianwen & Han, Song & Rong, Na & Cao, Yun, 2023. "A hybrid transfer learning method for transient stability prediction considering sample imbalance," Applied Energy, Elsevier, vol. 333(C).
    4. Wang, Kai & Li, Kangnan & Du, Feng & Zhang, Xiang & Wang, Yanhai & Sun, Jiazhi, 2024. "Research on prediction model of coal spontaneous combustion temperature based on SSA-CNN," Energy, Elsevier, vol. 290(C).
    5. Yuan, Wenlin & Zhang, Shijie & Su, Chengguo & Wu, Yang & Yan, Denghua & Wu, Zening, 2022. "Optimal scheduling of cascade hydropower plants in a portfolio electricity market considering the dynamic water delay," Energy, Elsevier, vol. 252(C).
    6. Xiang, Ling & Fu, Xiaomengting & Yao, Qingtao & Zhu, Guopeng & Hu, Aijun, 2024. "A novel model for ultra-short term wind power prediction based on Vision Transformer," Energy, Elsevier, vol. 294(C).
    7. Ghimire, Sujan & Nguyen-Huy, Thong & AL-Musaylh, Mohanad S. & Deo, Ravinesh C. & Casillas-Pérez, David & Salcedo-Sanz, Sancho, 2023. "A novel approach based on integration of convolutional neural networks and echo state network for daily electricity demand prediction," Energy, Elsevier, vol. 275(C).
    8. 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).
    9. Wang, Min & Rao, Congjun & Xiao, Xinping & Hu, Zhuo & Goh, Mark, 2024. "Efficient shrinkage temporal convolutional network model for photovoltaic power prediction," Energy, Elsevier, vol. 297(C).
    10. Li, Boda & Chen, Ying & Wei, Wei & Hou, Yunhe & Mei, Shengwei, 2022. "Enhancing resilience of emergency heat and power supply via deployment of LNG tube trailers: A mean-risk optimization approach," Applied Energy, Elsevier, vol. 318(C).
    11. Yang, Mao & Wang, Tiancheng & Zhang, Xiaobin & Zhang, Wei & Wang, Bo, 2024. "Considering dynamic perception of fluctuation trend for long-foresight-term wind power prediction," Energy, Elsevier, vol. 289(C).
    12. Xu, Huifeng & Hu, Feihu & Liang, Xinhao & Zhao, Guoqing & Abugunmi, Mohammad, 2024. "A framework for electricity load forecasting based on attention mechanism time series depthwise separable convolutional neural network," Energy, Elsevier, vol. 299(C).
    13. Li, Jiawen & Yu, Tao & Zhang, Xiaoshun, 2022. "Coordinated load frequency control of multi-area integrated energy system using multi-agent deep reinforcement learning," Applied Energy, Elsevier, vol. 306(PA).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhang, Qijun & Liu, Yuxin & Fu, Zhipeng & Cao, Shengliang & Li, Zhen & Wang, Zhenzhen & Wang, Jinshi, 2026. "Multi-load forecasting for regional integrated energy systems using a hybrid ResNet-GRU-MTL model with dynamic grey wolf optimization," Renewable Energy, Elsevier, vol. 256(PI).
    2. Yu, Bosheng & Liu, Wenhe & Xie, Daxing & Cui, Xiao & Zhang, Huisheng, 2025. "A novel gas turbine performance prediction model incorporating the residual connection and feature engineering methods," Energy, Elsevier, vol. 332(C).

    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. Yin, Linfei & Ju, Linyi, 2025. "ShuffleTransformerMulti-headAttentionNet network for user load forecasting," Energy, Elsevier, vol. 322(C).
    2. Lu, Quan & Zeng, Haozheng & Yin, Linfei, 2025. "Dynamic distributed multi-objective mantis search algorithm based on Transformer hybrid strategy for novel power system dispatch," Energy, Elsevier, vol. 324(C).
    3. Yang, Mao & Guo, Yunfeng & Huang, Tao & Zhang, Wei, 2025. "Power prediction considering NWP wind speed error tolerability: A strategy to improve the accuracy of short-term wind power prediction under wind speed offset scenarios," Applied Energy, Elsevier, vol. 377(PD).
    4. Huang, Wenxuan & Yin, Linfei, 2025. "Large-scale model driven real-time economic generation control for integrated energy systems," Applied Energy, Elsevier, vol. 401(PB).
    5. 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 sludge)," Energy, Elsevier, vol. 308(C).
    6. Ivan S. Maksymov, 2023. "Analogue and Physical Reservoir Computing Using Water Waves: Applications in Power Engineering and Beyond," Energies, MDPI, vol. 16(14), pages 1-26, July.
    7. Yu, Sheng & He, Bin & Fang, Lei, 2025. "Multi-step short-term forecasting of photovoltaic power utilizing TimesNet with enhanced feature extraction and a novel loss function," Applied Energy, Elsevier, vol. 388(C).
    8. 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).
    9. Jia, Min & Zhang, Zhe & Zhang, Li & Zhao, Liang & Lu, Xinbo & Li, Linyan & Ruan, Jianhui & Wu, Yunlong & He, Zhuoming & Liu, Mei & Jiang, Lingling & Gao, Yajing & Wu, Pengcheng & Zhu, Shuying & Niu, M, 2024. "Optimization of electricity generation and assessment of provincial grid emission factors from 2020 to 2060 in China," Applied Energy, Elsevier, vol. 373(C).
    10. Liangcai Zhou & Long Huo & Linlin Liu & Hao Xu & Rui Chen & Xin Chen, 2025. "Optimal Power Flow for High Spatial and Temporal Resolution Power Systems with High Renewable Energy Penetration Using Multi-Agent Deep Reinforcement Learning," Energies, MDPI, vol. 18(7), pages 1-14, April.
    11. Wenting Zhao & Haoran Xu & Peng Chen & Juan Zhang & Jing Li & Tingting Cai, 2025. "Elastic Momentum-Enhanced Adaptive Hybrid Method for Short-Term Load Forecasting," Energies, MDPI, vol. 18(13), pages 1-25, June.
    12. Rongqi Zhang & Shanghong Zhang & Xiaoxiong Wen & Zhu Jing, 2023. "Refined Scheduling Based on Dynamic Capacity Model for Short-term Hydropower Generation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(1), pages 21-35, January.
    13. Su, Chengguo & Li, Li & Zhang, Taiheng & Sui, Quan & Yang, Yunbo, 2025. "A real-time scheduling framework of cascade hydropower-photovoltaic power complementary systems based on model predictive control," Applied Energy, Elsevier, vol. 392(C).
    14. 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).
    15. Xuezhao Zhang & Zijie Chen & Wenxiao Wang & Xiaofen Fang, 2024. "Prediction Method of PHEV Driving Energy Consumption Based on the Optimized CNN BiLSTM Attention Network," Energies, MDPI, vol. 17(12), pages 1-21, June.
    16. Li, Jianfang & Jia, Li & Zhou, Chengyu, 2024. "Probability density function based adaptive ensemble learning with global convergence for wind power prediction," Energy, Elsevier, vol. 312(C).
    17. Zhang, Tian & Hou, Zhengmeng & Li, Xiaoqin & Chen, Qianjun & Wang, Qichen & Lüddeke, Christian & Wu, Lin & Wu, Xuning & Sun, Wei, 2025. "A novel multivariable prognostic approach for PEMFC degradation and remaining useful life prediction using random forest and temporal convolutional network," Applied Energy, Elsevier, vol. 385(C).
    18. Xinghua Liu & Siwei Qiao & Zhiwei Liu, 2023. "A Survey on Load Frequency Control of Multi-Area Power Systems: Recent Challenges and Strategies," Energies, MDPI, vol. 16(5), pages 1-22, February.
    19. Xiao, Yulong & Li, Chaoshun & Li, Shiqi & Zhu, Qiannan & Chang, Pengxia & Wu, Ting, 2025. "Bidding-clearing-scheduling coordinated optimization for cascade hydro-wind-photovoltaic alliances in electricity spot markets: A multi-stage framework," Energy, Elsevier, vol. 335(C).
    20. Pesantez, Jorge E. & Li, Binbin & Lee, Christopher & Zhao, Zhizhen & Butala, Mark & Stillwell, Ashlynn S., 2023. "A Comparison Study of Predictive Models for Electricity Demand in a Diverse Urban Environment," Energy, Elsevier, vol. 283(C).

    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:309:y:2024:i:c:s0360544224029384. 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.