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Reactive Power Optimization of a Distribution System Based on Scene Matching and Deep Belief Network

Author

Listed:
  • Junyong Wu

    (School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Chen Shi

    (School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Meiyang Shao

    (School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Ran An

    (School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Xiaowen Zhu

    (School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Xing Huang

    (ABB China Ltd., Beijing 100015, China)

  • Rong Cai

    (ABB China Ltd., Beijing 100015, China)

Abstract

With a large number of distributed generators (DGs) and electrical vehicles (EVs) integrated into the power distribution system, the complexity of distribution system operation is increased, which arises to higher requirements for online reactive power optimization. This paper proposes two methods for online reactive power optimization, a scene-matching method based on Random Matrix (RM) features and a deep learning method based on Deep Belief Network (DBN). Firstly, utilizing the operation and ambient Big Data (BD) of the distribution system, we construct the high-dimension Random Matrices and extract 57 state features for the subsequent scene-matching and DBN training. Secondly, the feature-based scene-matching method is proposed. Furtherly, to effectively deal with the uncertainty of DGs and to avoid the performance deterioration of the scene-matching method under a new unknown scene, the DBN-based model is constructed and trained, with the former features as the inputs and the conventional reactive power control solutions as the outputs. This DBN model can learn the nonlinear complicated relationship between the system features and the reactive power control solutions. Finally, the comprehensive case studies have been conducted on the modified IEEE-37 nodes active distribution system, and the performances of the proposed two methods are compared with the conventional method. The results show that the DBN-based method possesses the better performance than the others, and it can reduce the network losses and node voltage deviations obviously, even under the new unknown and unmatched scenes. It does not depend on the distribution system model and parameters anymore and can provide online decision-making more quickly. The discussions of the two methods under different DG penetrations and the historical data volume were given, verifying the adaptability, robustness and generalization ability of the DBN-based method.

Suggested Citation

  • Junyong Wu & Chen Shi & Meiyang Shao & Ran An & Xiaowen Zhu & Xing Huang & Rong Cai, 2019. "Reactive Power Optimization of a Distribution System Based on Scene Matching and Deep Belief Network," Energies, MDPI, vol. 12(17), pages 1-24, August.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:17:p:3246-:d:260213
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    References listed on IDEAS

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    1. Shunjiang Lin & Sen He & Haipeng Zhang & Mingbo Liu & Zhiqiang Tang & Hao Jiang & Yunong Song, 2019. "Robust Optimal Allocation of Decentralized Reactive Power Compensation in Three-Phase Four-Wire Low-Voltage Distribution Networks Considering the Uncertainty of Photovoltaic Generation," Energies, MDPI, vol. 12(13), pages 1-20, June.
    2. Zhao, Yang & Li, Jianping & Yu, Lean, 2017. "A deep learning ensemble approach for crude oil price forecasting," Energy Economics, Elsevier, vol. 66(C), pages 9-16.
    3. Jun Xie & Chunxiang Liang & Yichen Xiao, 2018. "Reactive Power Optimization for Distribution Network Based on Distributed Random Gradient-Free Algorithm," Energies, MDPI, vol. 11(3), pages 1-13, March.
    4. Junwei Cao & Wanlu Zhang & Zeqing Xiao & Haochen Hua, 2019. "Reactive Power Optimization for Transient Voltage Stability in Energy Internet via Deep Reinforcement Learning Approach," Energies, MDPI, vol. 12(8), pages 1-17, April.
    5. Wang, Huai-zhi & Li, Gang-qiang & Wang, Gui-bin & Peng, Jian-chun & Jiang, Hui & Liu, Yi-tao, 2017. "Deep learning based ensemble approach for probabilistic wind power forecasting," Applied Energy, Elsevier, vol. 188(C), pages 56-70.
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    Cited by:

    1. Seok-Il Go & Sang-Yun Yun & Seon-Ju Ahn & Joon-Ho Choi, 2020. "Voltage and Reactive Power Optimization Using a Simplified Linear Equations at Distribution Networks with DG," Energies, MDPI, vol. 13(13), pages 1-23, June.
    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).

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