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Meta-learning based voltage control strategy for emergency faults of active distribution networks

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  • Zhao, Yincheng
  • Zhang, Guozhou
  • Hu, Weihao
  • Huang, Qi
  • Chen, Zhe
  • Blaabjerg, Frede

Abstract

With the increase of energy demand and the continuous development of renewable energy technology, active distribution networks have become increasingly important. However, the introduction of a large amount of renewable energy has made the structure of ADN increasingly complex and fragile, and emergency fault caused by emergencies may often occur. Voltage control in the emergency fault event is particularly important. In this context, this paper presents a meta-learning based voltage control strategy for renewable energy integrated active distribution network. A general regression neural network is first applied to extract features from the operation data. Then, the local cross-channel interaction network is adopted to capture targeted information that is most related to emergency fault from the features and induce knowledge transfer to update the voltage control strategy. This allows the proposed strategy to make optimal decisions quickly when only limited data are available under an emergency fault that has never occurred. Comparison results based on a 69-bus distribution network validate the effectiveness and robustness of the proposed strategy.

Suggested Citation

  • Zhao, Yincheng & Zhang, Guozhou & Hu, Weihao & Huang, Qi & Chen, Zhe & Blaabjerg, Frede, 2023. "Meta-learning based voltage control strategy for emergency faults of active distribution networks," Applied Energy, Elsevier, vol. 349(C).
    • Handle: RePEc:eee:appene:v:349:y:2023:i:c:s0306261923007638
    DOI: 10.1016/j.apenergy.2023.121399
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    1. Pilotti, L. & Colombari, M. & Castelli, A.F. & Binotti, M. & Giaconia, A. & Martelli, E., 2023. "Simultaneous design and operational optimization of hybrid CSP-PV plants," Applied Energy, Elsevier, vol. 331(C).
    2. Brusaferri, Alessandro & Matteucci, Matteo & Portolani, Pietro & Vitali, Andrea, 2019. "Bayesian deep learning based method for probabilistic forecast of day-ahead electricity prices," Applied Energy, Elsevier, vol. 250(C), pages 1158-1175.
    3. Huang, Yan & Ju, Yuntao & Ma, Kang & Short, Michael & Chen, Tao & Zhang, Ruosi & Lin, Yi, 2022. "Three-phase optimal power flow for networked microgrids based on semidefinite programming convex relaxation," Applied Energy, Elsevier, vol. 305(C).
    4. Niknam, Taher & Firouzi, Bahman Bahmani & Ostadi, Amir, 2010. "A new fuzzy adaptive particle swarm optimization for daily Volt/Var control in distribution networks considering distributed generators," Applied Energy, Elsevier, vol. 87(6), pages 1919-1928, June.
    5. Xiang, Yue & Lu, Yu & Liu, Junyong, 2023. "Deep reinforcement learning based topology-aware voltage regulation of distribution networks with distributed energy storage," Applied Energy, Elsevier, vol. 332(C).
    6. Niu, Tong & Li, Jinkai & Wei, Wei & Yue, Hui, 2022. "A hybrid deep learning framework integrating feature selection and transfer learning for multi-step global horizontal irradiation forecasting," Applied Energy, Elsevier, vol. 326(C).
    7. Kabir, Farzana & Yu, Nanpeng & Gao, Yuanqi & Wang, Wenyu, 2023. "Deep reinforcement learning-based two-timescale Volt-VAR control with degradation-aware smart inverters in power distribution systems," Applied Energy, Elsevier, vol. 335(C).
    8. Luo, Shihua & Hu, Weihao & Liu, Wen & Zhang, Zhenyuan & Bai, Chunguang & Huang, Qi & Chen, Zhe, 2022. "Study on the decarbonization in China's power sector under the background of carbon neutrality by 2060," Renewable and Sustainable Energy Reviews, Elsevier, vol. 166(C).
    9. Zhu, Ziqing & Wing Chan, Ka & Bu, Siqi & Zhou, Bin & Xia, Shiwei, 2021. "Real-Time interaction of active distribution network and virtual microgrids: Market paradigm and data-driven stakeholder behavior analysis," Applied Energy, Elsevier, vol. 297(C).
    10. Zhu, Ziqing & Hu, Ze & Chan, Ka Wing & Bu, Siqi & Zhou, Bin & Xia, Shiwei, 2023. "Reinforcement learning in deregulated energy market: A comprehensive review," Applied Energy, Elsevier, vol. 329(C).
    11. Luo, Shihua & Hu, Weihao & Liu, Wen & Xu, Xiao & Huang, Qi & Chen, Zhe & Lund, Henrik, 2021. "Transition pathways towards a deep decarbonization energy system—A case study in Sichuan, China," Applied Energy, Elsevier, vol. 302(C).
    12. Gao, Yuanqi & Yu, Nanpeng, 2022. "Model-augmented safe reinforcement learning for Volt-VAR control in power distribution networks," Applied Energy, Elsevier, vol. 313(C).
    13. Oh, Seok Hwa & Yoon, Yong Tae & Kim, Seung Wan, 2020. "Online reconfiguration scheme of self-sufficient distribution network based on a reinforcement learning approach," Applied Energy, Elsevier, vol. 280(C).
    14. Hong, Ying-Yi & Rioflorido, Christian Lian Paulo P., 2019. "A hybrid deep learning-based neural network for 24-h ahead wind power forecasting," Applied Energy, Elsevier, vol. 250(C), pages 530-539.
    15. Liang, Xinbin & Liu, Zhuoxuan & Wang, Jie & Jin, Xinqiao & Du, Zhimin, 2023. "Uncertainty quantification-based robust deep learning for building energy systems considering distribution shift problem," Applied Energy, Elsevier, vol. 337(C).
    16. Cao, Di & Zhao, Junbo & Hu, Weihao & Ding, Fei & Yu, Nanpeng & Huang, Qi & Chen, Zhe, 2022. "Model-free voltage control of active distribution system with PVs using surrogate model-based deep reinforcement learning," Applied Energy, Elsevier, vol. 306(PA).
    17. Wasiak, Irena & Szypowski, Michał & Kelm, Paweł & Mieński, Rozmysław & Wędzik, Andrzej & Pawełek, Ryszard & Małaczek, Michał & Urbanek, Przemysław, 2022. "Innovative energy management system for low-voltage networks with distributed generation based on prosumers’ active participation," Applied Energy, Elsevier, vol. 312(C).
    18. Wang, Yi & Qiu, Dawei & Sun, Mingyang & Strbac, Goran & Gao, Zhiwei, 2023. "Secure energy management of multi-energy microgrid: A physical-informed safe reinforcement learning approach," Applied Energy, Elsevier, vol. 335(C).
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    Cited by:

    1. Mario Versaci & Fabio La Foresta, 2024. "Fuzzy Approach for Managing Renewable Energy Flows for DC-Microgrid with Composite PV-WT Generators and Energy Storage System," Energies, MDPI, vol. 17(2), pages 1-31, January.
    2. Zhang, Xiao & Wu, Zhi & Sun, Qirun & Gu, Wei & Zheng, Shu & Zhao, Jingtao, 2024. "Application and progress of artificial intelligence technology in the field of distribution network voltage Control：A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    3. Zhiye Lu & Lishu Wang & Panbao Wang, 2023. "Review of Voltage Control Strategies for DC Microgrids," Energies, MDPI, vol. 16(17), pages 1-19, August.

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