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Small-Signal Stability Constrained Optimal Power Flow Model Based on BP Neural Network Algorithm

Author

Listed:
  • Yude Yang

    (Guangxi Key Laboratory of Power System Optimization and Energy-Saving Technology, School of Electrical Engineering, Guangxi University, Nanning 530004, China)

  • Yuying Luo

    (Guangxi Key Laboratory of Power System Optimization and Energy-Saving Technology, School of Electrical Engineering, Guangxi University, Nanning 530004, China)

  • Lizhen Yang

    (School of Economics and Management, Guangxi Vocational University of Agriculture, Nanning 530004, China)

Abstract

The existing small-signal stability constrained optimal power flow (SC-OPF) generally needs to deduce the sensitivity analytical expression of the small-signal stability index to parameters, which requires a large amount of formula derivation and mathematical computation. In order to overcome the complex problem of sensitivity, this article proposes an approximate sensitivity calculation method based on the back propagation (BP) neural network algorithm in the SC-OPF model. First, the minimum damping ratio of the system is taken as the small-signal stability index, and the algebraic inequality composed of the minimum damping ratio is used as the small-signal stability constraint in this model. Second, the BP neural network is introduced into the SC-OPF to analyze the mapping relationship between the generator power, node power, line power and the minimum damping ratio of the system, and then the numerical differentiation method is used to calculate the approximate first-order sensitivity of the minimum damping ratio in the correction equation. Finally, a series of simulations on the WSCC-9 bus and IEEE-39 bus systems verify the correctness and effectiveness of the proposed model.

Suggested Citation

  • Yude Yang & Yuying Luo & Lizhen Yang, 2022. "Small-Signal Stability Constrained Optimal Power Flow Model Based on BP Neural Network Algorithm," Sustainability, MDPI, vol. 14(20), pages 1-14, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:20:p:13386-:d:945061
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    References listed on IDEAS

    as
    1. Thanh Long Duong & Ngoc Anh Nguyen & Thuan Thanh Nguyen, 2020. "A Newly Hybrid Method Based on Cuckoo Search and Sunflower Optimization for Optimal Power Flow Problem," Sustainability, MDPI, vol. 12(13), pages 1-19, June.
    2. Mahmoud A. Ali & Salah Kamel & Mohamed H. Hassan & Emad M. Ahmed & Mohana Alanazi, 2022. "Optimal Power Flow Solution of Power Systems with Renewable Energy Sources Using White Sharks Algorithm," Sustainability, MDPI, vol. 14(10), pages 1-21, May.
    3. Xiaomin Xu & Luyao Peng & Zhengsen Ji & Shipeng Zheng & Zhuxiao Tian & Shiping Geng, 2021. "Research on Substation Project Cost Prediction Based on Sparrow Search Algorithm Optimized BP Neural Network," Sustainability, MDPI, vol. 13(24), pages 1-17, December.
    4. Li, Song & Goel, Lalit & Wang, Peng, 2016. "An ensemble approach for short-term load forecasting by extreme learning machine," Applied Energy, Elsevier, vol. 170(C), pages 22-29.
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