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Resilient power network structure for stable operation of energy systems: A transfer learning approach

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  • Huang, Wanjun
  • Zhang, Xinran
  • Zheng, Weiye

Abstract

With increasing dynamic loads, short-term voltage stability (STVS) problems are emerging in sub-transmission expansion planning (SEP), which threats the stable operation of energy systems. However, it is computationally intensive to evaluate all possible network structures in SEP, since STVS is traditionally analyzed for a fixed network structure at a certain operating condition using time-domain simulations. Taking advantage of big data analytics, a deep transfer learning approach based on bi-directional long short-term memory (BiLSTM) is proposed to identify resilient network structures with better STVS performance efficiently. First, an improved voltage recovery index (IVRI) is introduced to quantify the STVS of different network structures with a higher degree of distinguishment. Then, a BiLSTM-based STVS evaluation machine is devised to identify resilient network structures with better STVS performances with high efficiency, which predicts the STVS of various network structures without resorting to time-consuming time-domain simulations. Finally, the STVS evaluation machine is transferred to adapt to new systems with different numbers of buses in the context of SEP. Numerical tests on the IEEE benchmarks and the real Guangdong Power Grid have verified the effectiveness of the proposed approach. An illustrative application example indicates the potential of the proposed approach in tackling STVS-based SEP for the stable operation of energy systems.

Suggested Citation

  • Huang, Wanjun & Zhang, Xinran & Zheng, Weiye, 2021. "Resilient power network structure for stable operation of energy systems: A transfer learning approach," Applied Energy, Elsevier, vol. 296(C).
  • Handle: RePEc:eee:appene:v:296:y:2021:i:c:s0306261921005201
    DOI: 10.1016/j.apenergy.2021.117065
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    References listed on IDEAS

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    1. Ibrahim, Muhammad Sohail & Dong, Wei & Yang, Qiang, 2020. "Machine learning driven smart electric power systems: Current trends and new perspectives," Applied Energy, Elsevier, vol. 272(C).
    2. Shi, Zhongtuo & Yao, Wei & Li, Zhouping & Zeng, Lingkang & Zhao, Yifan & Zhang, Runfeng & Tang, Yong & Wen, Jinyu, 2020. "Artificial intelligence techniques for stability analysis and control in smart grids: Methodologies, applications, challenges and future directions," Applied Energy, Elsevier, vol. 278(C).
    3. Shi, Zhongtuo & Yao, Wei & Zeng, Lingkang & Wen, Jianfeng & Fang, Jiakun & Ai, Xiaomeng & Wen, Jinyu, 2020. "Convolutional neural network-based power system transient stability assessment and instability mode prediction," Applied Energy, Elsevier, vol. 263(C).
    4. Ye, Chengjin & Ding, Yi & Song, Yonghua & Lin, Zhenzhi & Wang, Lei, 2018. "A data driven multi-state model for distribution system flexible planning utilizing hierarchical parallel computing," Applied Energy, Elsevier, vol. 232(C), pages 9-25.
    5. Zheng, Weiye & Hill, David J., 2021. "Incentive-based coordination mechanism for distributed operation of integrated electricity and heat systems," Applied Energy, Elsevier, vol. 285(C).
    6. Gitizadeh, Mohsen & Vahed, Ali Azizi & Aghaei, Jamshid, 2013. "Multistage distribution system expansion planning considering distributed generation using hybrid evolutionary algorithms," Applied Energy, Elsevier, vol. 101(C), pages 655-666.
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    Cited by:

    1. 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).

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