IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i20p13386-d945061.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/20/13386/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/20/13386/
    Download Restriction: no
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. V. Y. Kondaiah & B. Saravanan, 2022. "Short-Term Load Forecasting with a Novel Wavelet-Based Ensemble Method," Energies, MDPI, vol. 15(14), pages 1-17, July.
    2. Kailai Ni & Jianzhou Wang & Guangyu Tang & Danxiang Wei, 2019. "Research and Application of a Novel Hybrid Model Based on a Deep Neural Network for Electricity Load Forecasting: A Case Study in Australia," Energies, MDPI, vol. 12(13), pages 1-30, June.
    3. Rendon-Sanchez, Juan F. & de Menezes, Lilian M., 2019. "Structural combination of seasonal exponential smoothing forecasts applied to load forecasting," European Journal of Operational Research, Elsevier, vol. 275(3), pages 916-924.
    4. Qu, Zhijian & Xu, Juan & Wang, Zixiao & Chi, Rui & Liu, Hanxin, 2021. "Prediction of electricity generation from a combined cycle power plant based on a stacking ensemble and its hyperparameter optimization with a grid-search method," Energy, Elsevier, vol. 227(C).
    5. He, Yan & Zhang, Hongli & Dong, Yingchao & Wang, Cong & Ma, Ping, 2024. "Residential net load interval prediction based on stacking ensemble learning," Energy, Elsevier, vol. 296(C).
    6. Cocco Mariani, Viviana & Hennings Och, Stephan & dos Santos Coelho, Leandro & Domingues, Eric, 2019. "Pressure prediction of a spark ignition single cylinder engine using optimized extreme learning machine models," Applied Energy, Elsevier, vol. 249(C), pages 204-221.
    7. Sun, Wei & Zhang, Chongchong, 2018. "Analysis and forecasting of the carbon price using multi—resolution singular value decomposition and extreme learning machine optimized by adaptive whale optimization algorithm," Applied Energy, Elsevier, vol. 231(C), pages 1354-1371.
    8. Xing Zhang & Zhuoqun Wei, 2019. "A Hybrid Model Based on Principal Component Analysis, Wavelet Transform, and Extreme Learning Machine Optimized by Bat Algorithm for Daily Solar Radiation Forecasting," Sustainability, MDPI, vol. 11(15), pages 1-20, July.
    9. Sepasi, Saeed & Reihani, Ehsan & Howlader, Abdul M. & Roose, Leon R. & Matsuura, Marc M., 2017. "Very short term load forecasting of a distribution system with high PV penetration," Renewable Energy, Elsevier, vol. 106(C), pages 142-148.
    10. He, Feifei & Zhou, Jianzhong & Mo, Li & Feng, Kuaile & Liu, Guangbiao & He, Zhongzheng, 2020. "Day-ahead short-term load probability density forecasting method with a decomposition-based quantile regression forest," Applied Energy, Elsevier, vol. 262(C).
    11. Zhineng Hu & Jing Ma & Liangwei Yang & Liming Yao & Meng Pang, 2019. "Monthly electricity demand forecasting using empirical mode decomposition-based state space model," Energy & Environment, , vol. 30(7), pages 1236-1254, November.
    12. Xueliang Li & Bingkang Li & Long Zhao & Huiru Zhao & Wanlei Xue & Sen Guo, 2019. "Forecasting the Short-Term Electric Load Considering the Influence of Air Pollution Prevention and Control Policy via a Hybrid Model," Sustainability, MDPI, vol. 11(10), pages 1-21, May.
    13. Federico Divina & Aude Gilson & Francisco Goméz-Vela & Miguel García Torres & José F. Torres, 2018. "Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting," Energies, MDPI, vol. 11(4), pages 1-31, April.
    14. Talaat, M. & Farahat, M.A. & Mansour, Noura & Hatata, A.Y., 2020. "Load forecasting based on grasshopper optimization and a multilayer feed-forward neural network using regressive approach," Energy, Elsevier, vol. 196(C).
    15. Sen Wang & Yonghui Sun & Yan Zhou & Rabea Jamil Mahfoud & Dongchen Hou, 2019. "A New Hybrid Short-Term Interval Forecasting of PV Output Power Based on EEMD-SE-RVM," Energies, MDPI, vol. 13(1), pages 1-17, December.
    16. Shan, Rui & Sasthav, Colin & Wang, Xianxun & Lima, Luana M.M., 2020. "Complementary relationship between small-hydropower and increasing penetration of solar photovoltaics: Evidence from CAISO," Renewable Energy, Elsevier, vol. 155(C), pages 1139-1146.
    17. Hu, Yi & Qu, Boyang & Wang, Jie & Liang, Jing & Wang, Yanli & Yu, Kunjie & Li, Yaxin & Qiao, Kangjia, 2021. "Short-term load forecasting using multimodal evolutionary algorithm and random vector functional link network based ensemble learning," Applied Energy, Elsevier, vol. 285(C).
    18. Meira, Erick & Cyrino Oliveira, Fernando Luiz & de Menezes, Lilian M., 2021. "Point and interval forecasting of electricity supply via pruned ensembles," Energy, Elsevier, vol. 232(C).
    19. Chan-Uk Yeom & Keun-Chang Kwak, 2017. "Short-Term Electricity-Load Forecasting Using a TSK-Based Extreme Learning Machine with Knowledge Representation," Energies, MDPI, vol. 10(10), pages 1-18, October.
    20. Wenhui Zhao & Tong Li & Danyang Xu & Zhaohua Wang, 2024. "A global forecasting method of heterogeneous household short-term load based on pre-trained autoencoder and deep-LSTM model," Annals of Operations Research, Springer, vol. 339(1), pages 227-259, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:20:p:13386-:d:945061. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.