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Data-Driven Dynamic Stability Assessment in Large-Scale Power Grid Based on Deep Transfer Learning

Author

Listed:
  • Weijia Wen

    (State Grid Hunan Information & Telecommunication Company, Changsha 410004, China)

  • Xiao Ling

    (State Grid Hunan Information & Telecommunication Company, Changsha 410004, China)

  • Jianxin Sui

    (State Grid Hunan Information & Telecommunication Company, Changsha 410004, China)

  • Junjie Lin

    (School of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China)

Abstract

For data-driven dynamic stability assessment (DSA) in modern power grids, DSA models generally have to be learned from scratch when faced with new grids, resulting in high offline computational costs. To tackle this undesirable yet often overlooked problem, this work develops a light-weight framework for DSA-oriented stability knowledge transfer from off-the-shelf test systems to practical power grids. A scale-free system feature learner is proposed to characterize system-wide features of various systems in a unified manner. Given a real-world power grid for DSA, selective stability knowledge transfer is intelligently carried out by comparing system similarities between it and the available test systems. Afterward, DSA model fine-tuning is performed to make the transferred knowledge adapt well to practical DSA contexts. Numerical test results on a realistic system, i.e., the provincial GD Power Grid in China, verify the effectiveness of the proposed framework.

Suggested Citation

  • Weijia Wen & Xiao Ling & Jianxin Sui & Junjie Lin, 2023. "Data-Driven Dynamic Stability Assessment in Large-Scale Power Grid Based on Deep Transfer Learning," Energies, MDPI, vol. 16(3), pages 1-15, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1142-:d:1042116
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    References listed on IDEAS

    as
    1. Estefania Alexandra Tapia & Delia Graciela Colomé & José Luis Rueda Torres, 2022. "Recurrent Convolutional Neural Network-Based Assessment of Power System Transient Stability and Short-Term Voltage Stability," Energies, MDPI, vol. 15(23), pages 1-24, December.
    2. Md. Nazmul Hasan & Rafia Nishat Toma & Abdullah-Al Nahid & M M Manjurul Islam & Jong-Myon Kim, 2019. "Electricity Theft Detection in Smart Grid Systems: A CNN-LSTM Based Approach," Energies, MDPI, vol. 12(17), pages 1-18, August.
    3. Raoult Teukam Dabou & Innocent Kamwa & Jacques Tagoudjeu & Francis Chuma Mugombozi, 2021. "Sparse Signal Reconstruction on Fixed and Adaptive Supervised Dictionary Learning for Transient Stability Assessment," Energies, MDPI, vol. 14(23), pages 1-20, November.
    4. Nallapaneni Manoj Kumar & Aneesh A. Chand & Maria Malvoni & Kushal A. Prasad & Kabir A. Mamun & F.R. Islam & Shauhrat S. Chopra, 2020. "Distributed Energy Resources and the Application of AI, IoT, and Blockchain in Smart Grids," Energies, MDPI, vol. 13(21), pages 1-42, November.
    5. Petar Sarajcev & Antonijo Kunac & Goran Petrovic & Marin Despalatovic, 2021. "Power System Transient Stability Assessment Using Stacked Autoencoder and Voting Ensemble," Energies, MDPI, vol. 14(11), pages 1-26, May.
    6. Mohammad Shoaib Shahriar & Ibrahim Omar Habiballah & Huthaifa Hussein, 2018. "Optimization of Phasor Measurement Unit (PMU) Placement in Supervisory Control and Data Acquisition (SCADA)-Based Power System for Better State-Estimation Performance," Energies, MDPI, vol. 11(3), pages 1-15, March.
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