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Deep learning hybrid method for islanding detection in distributed generation

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  • Kong, Xiangrui
  • Xu, Xiaoyuan
  • Yan, Zheng
  • Chen, Sijie
  • Yang, Huoming
  • Han, Dong

Abstract

The increasing penetration of distributed energy brings significant uncertainty and noises to microgrid operation, which enlarge the difficulty of microgrid monitoring. For as much as the detection of islanding is prone to be interfered by grid disturbance, island detection device may make misjudgment thus causing the consequence of distributed generations (DGs) out of service. The detection device must provide with the ability to differ islanding from grid disturbance. In this paper, the concept of deep learning is introduced into the classification of islanding and grid disturbance for the first time. A novel deep learning framework is proposed to detect and classify islanding or grid disturbance. The framework is a hybrid of wavelet transformation, multi-resolution singular spectrum entropy, and deep learning architecture. As a signal processing method after wavelet transformation, multi-resolution singular spectrum entropy combines multi-resolution analysis and spectrum analysis with entropy as output, from which we can extract the intrinsic different features between islanding and grid disturbance. With the features extracted, a deep learning based algorithm is proposed to classify islanding and grid disturbance. Simulation results indicate that the method can achieve its goal while being highly accurate, so the DGs mistakenly withdrawing from power grids can be avoided.

Suggested Citation

  • Kong, Xiangrui & Xu, Xiaoyuan & Yan, Zheng & Chen, Sijie & Yang, Huoming & Han, Dong, 2018. "Deep learning hybrid method for islanding detection in distributed generation," Applied Energy, Elsevier, vol. 210(C), pages 776-785.
    • Handle: RePEc:eee:appene:v:210:y:2018:i:c:p:776-785
    DOI: 10.1016/j.apenergy.2017.08.014
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    References listed on IDEAS

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    Cited by:

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    5. Suryanarayana, Gowri & Lago, Jesus & Geysen, Davy & Aleksiejuk, Piotr & Johansson, Christian, 2018. "Thermal load forecasting in district heating networks using deep learning and advanced feature selection methods," Energy, Elsevier, vol. 157(C), pages 141-149.
    6. Hu, R.L. & Granderson, J. & Auslander, D.M. & Agogino, A., 2019. "Design of machine learning models with domain experts for automated sensor selection for energy fault detection," Applied Energy, Elsevier, vol. 235(C), pages 117-128.
    7. Ming Li & Anqing Chen & Peixiong Liu & Wenbo Ren & Chenghao Zheng, 2024. "Multivariable Algorithm Using Signal-Processing Techniques to Identify Islanding Events in Utility Grid with Renewable Energy Penetration," Energies, MDPI, vol. 17(4), pages 1-26, February.
    8. Mohammad Abu Sarhan, 2023. "An Extensive Review and Analysis of Islanding Detection Techniques in DG Systems Connected to Power Grids," Energies, MDPI, vol. 16(9), pages 1-22, April.
    9. Masoud Ahmadipour & Hashim Hizam & Mohammad Lutfi Othman & Mohd Amran Mohd Radzi & Nikta Chireh, 2019. "A Fast Fault Identification in a Grid-Connected Photovoltaic System Using Wavelet Multi-Resolution Singular Spectrum Entropy and Support Vector Machine," Energies, MDPI, vol. 12(13), pages 1-18, June.
    10. Yan Xia & Feihong Yu & Xingzhong Xiong & Qinyuan Huang & Qijun Zhou, 2022. "A Novel Microgrid Islanding Detection Algorithm Based on a Multi-Feature Improved LSTM," Energies, MDPI, vol. 15(8), pages 1-24, April.
    11. Patnaik, Bhaskar & Mishra, Manohar & Bansal, Ramesh C. & Jena, Ranjan Kumar, 2020. "AC microgrid protection – A review: Current and future prospective," Applied Energy, Elsevier, vol. 271(C).
    12. Ahmadipour, Masoud & Hizam, Hashim & Othman, Mohammad Lutfi & Radzi, Mohd Amran Mohd & Murthy, Avinash Srikanta, 2018. "Islanding detection technique using Slantlet Transform and Ridgelet Probabilistic Neural Network in grid-connected photovoltaic system," Applied Energy, Elsevier, vol. 231(C), pages 645-659.
    13. Huang, Tian-en & Guo, Qinglai & Sun, Hongbin & Tan, Chin-Woo & Hu, Tianyu, 2019. "A deep spatial-temporal data-driven approach considering microclimates for power system security assessment," Applied Energy, Elsevier, vol. 237(C), pages 36-48.
    14. Khan, Mohammed Ali & Haque, Ahteshamul & Kurukuru, V.S. Bharath & Saad, Mekhilef, 2022. "Islanding detection techniques for grid-connected photovoltaic systems-A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    15. Danxiang Wei & Jianzhou Wang & Kailai Ni & Guangyu Tang, 2019. "Research and Application of a Novel Hybrid Model Based on a Deep Neural Network Combined with Fuzzy Time Series for Energy Forecasting," Energies, MDPI, vol. 12(18), pages 1-38, September.
    16. Masoud Ahmadipour & Hashim Hizam & Mohammad Lutfi Othman & Mohd Amran Mohd Radzi, 2018. "An Anti-Islanding Protection Technique Using a Wavelet Packet Transform and a Probabilistic Neural Network," Energies, MDPI, vol. 11(10), pages 1-31, October.

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