IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v165y2018ipbp1009-1023.html
   My bibliography  Save this article

An active perceivable device–oriented modeling framework for hydropower plant simulation

Author

Listed:
  • Zhang, Binqiao
  • Yuan, Xiaohui
  • Yuan, Yanbin
  • Wang, Xu

Abstract

To realize large-scale personalized customization of hydropower plant simulation system, this paper proposes active perceivable device–oriented modeling (APDOM) method from the perspective of dynamic coordination of intelligent hydropower devices. In APDOM, hydropower device is regarded as a basic modeling unit, which is endowed with intelligent perception and active service ability by integrating Service Oriented Architecture (SOA) and Event Driven Architecture (EDA). The direct coupling of models is eliminated so that it can execute on separate computers and achieves parallel distributed dynamic coordination. The modeling framework of supporting this method is provided, in which the definitions of core components such as active perceivable device, device bus, and perceivable message and its key implementation technologies are given. And the construction process of hydropower plant simulation based on this framework is demonstrated. Finally, an application instance of cascade hydropower plants illustrate that the APDOM and framework work effectively in solving large-scale customization development of hydropower plant simulation system.

Suggested Citation

  • Zhang, Binqiao & Yuan, Xiaohui & Yuan, Yanbin & Wang, Xu, 2018. "An active perceivable device–oriented modeling framework for hydropower plant simulation," Energy, Elsevier, vol. 165(PB), pages 1009-1023.
    • Handle: RePEc:eee:energy:v:165:y:2018:i:pb:p:1009-1023
    DOI: 10.1016/j.energy.2018.10.028
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544218320164
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2018.10.028?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yuan, Xiaohui & Tan, Qingxiong & Lei, Xiaohui & Yuan, Yanbin & Wu, Xiaotao, 2017. "Wind power prediction using hybrid autoregressive fractionally integrated moving average and least square support vector machine," Energy, Elsevier, vol. 129(C), pages 122-137.
    2. Yuan, Xiaohui & Chen, Zhihuan & Yuan, Yanbin & Huang, Yuehua, 2015. "Design of fuzzy sliding mode controller for hydraulic turbine regulating system via input state feedback linearization method," Energy, Elsevier, vol. 93(P1), pages 173-187.
    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. Chen, Zhihuan & Yuan, Xiaohui & Yuan, Yanbin & Lei, Xiaohui & Zhang, Binqiao, 2019. "Parameter estimation of fuzzy sliding mode controller for hydraulic turbine regulating system based on HICA algorithm," Renewable Energy, Elsevier, vol. 133(C), pages 551-565.
    2. Tingkai Gong & Xiaohui Yuan & Xu Wang & Yanbin Yuan & Binqiao Zhang, 2020. "Fault diagnosis for rolling element bearing using variational mode decomposition and l1 trend filtering," Journal of Risk and Reliability, , vol. 234(1), pages 116-128, February.
    3. Shengli Liao & Xudong Tian & Benxi Liu & Tian Liu & Huaying Su & Binbin Zhou, 2022. "Short-Term Wind Power Prediction Based on LightGBM and Meteorological Reanalysis," Energies, MDPI, vol. 15(17), pages 1-21, August.
    4. Kim, Deockho & Hur, Jin, 2018. "Short-term probabilistic forecasting of wind energy resources using the enhanced ensemble method," Energy, Elsevier, vol. 157(C), pages 211-226.
    5. Manisha Sawant & Rupali Patil & Tanmay Shikhare & Shreyas Nagle & Sakshi Chavan & Shivang Negi & Neeraj Dhanraj Bokde, 2022. "A Selective Review on Recent Advancements in Long, Short and Ultra-Short-Term Wind Power Prediction," Energies, MDPI, vol. 15(21), pages 1-24, October.
    6. Wang, Jujie & Li, Yaning, 2018. "Multi-step ahead wind speed prediction based on optimal feature extraction, long short term memory neural network and error correction strategy," Applied Energy, Elsevier, vol. 230(C), pages 429-443.
    7. Xing Zhang, 2018. "Short-Term Load Forecasting for Electric Bus Charging Stations Based on Fuzzy Clustering and Least Squares Support Vector Machine Optimized by Wolf Pack Algorithm," Energies, MDPI, vol. 11(6), pages 1-18, June.
    8. Yu, Xiaodong & Zhang, Jian & Fan, Chengyu & Chen, Sheng, 2016. "Stability analysis of governor-turbine-hydraulic system by state space method and graph theory," Energy, Elsevier, vol. 114(C), pages 613-622.
    9. Bai, Yulong & Liu, Ming-De & Ding, Lin & Ma, Yong-Jie, 2021. "Double-layer staged training echo-state networks for wind speed prediction using variational mode decomposition," Applied Energy, Elsevier, vol. 301(C).
    10. Liu, Xin & Cao, Zheming & Zhang, Zijun, 2021. "Short-term predictions of multiple wind turbine power outputs based on deep neural networks with transfer learning," Energy, Elsevier, vol. 217(C).
    11. Erhao Meng & Shengzhi Huang & Qiang Huang & Wei Fang & Hao Wang & Guoyong Leng & Lu Wang & Hao Liang, 2021. "A Hybrid VMD-SVM Model for Practical Streamflow Prediction Using an Innovative Input Selection Framework," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(4), pages 1321-1337, March.
    12. García-Nieto, P.J. & García-Gonzalo, E. & Fernández, J.R. Alonso & Muñiz, C. Díaz, 2019. "Modeling of the algal atypical increase in La Barca reservoir using the DE optimized least square support vector machine approach with feature selection," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 166(C), pages 461-480.
    13. Hu, Huanling & Wang, Lin & Lv, Sheng-Xiang, 2020. "Forecasting energy consumption and wind power generation using deep echo state network," Renewable Energy, Elsevier, vol. 154(C), pages 598-613.
    14. Madeline Hui Li Lee & Yee Chee Ser & Ganeshsree Selvachandran & Pham Huy Thong & Le Cuong & Le Hoang Son & Nguyen Trung Tuan & Vassilis C. Gerogiannis, 2022. "A Comparative Study of Forecasting Electricity Consumption Using Machine Learning Models," Mathematics, MDPI, vol. 10(8), pages 1-23, April.
    15. Farah, Shahid & David A, Wood & Humaira, Nisar & Aneela, Zameer & Steffen, Eger, 2022. "Short-term multi-hour ahead country-wide wind power prediction for Germany using gated recurrent unit deep learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    16. Zhang, Zhendong & Ye, Lei & Qin, Hui & Liu, Yongqi & Wang, Chao & Yu, Xiang & Yin, Xingli & Li, Jie, 2019. "Wind speed prediction method using Shared Weight Long Short-Term Memory Network and Gaussian Process Regression," Applied Energy, Elsevier, vol. 247(C), pages 270-284.
    17. Andrey Achitaev & Pavel Ilyushin & Konstantin Suslov & Sergey Kobyletski, 2022. "Dynamic Simulation of Starting and Emergency Conditions of a Hydraulic Unit Based on a Francis Turbine," Energies, MDPI, vol. 15(21), pages 1-18, October.
    18. González-Sopeña, J.M. & Pakrashi, V. & Ghosh, B., 2021. "An overview of performance evaluation metrics for short-term statistical wind power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    19. Yu, Guangzheng & Liu, Chengquan & Tang, Bo & Chen, Rusi & Lu, Liu & Cui, Chaoyue & Hu, Yue & Shen, Lingxu & Muyeen, S.M., 2022. "Short term wind power prediction for regional wind farms based on spatial-temporal characteristic distribution," Renewable Energy, Elsevier, vol. 199(C), pages 599-612.
    20. Huang, Sunhua & Xiong, Linyun & Wang, Jie & Li, Penghan & Wang, Ziqiang & Ma, Meilng, 2020. "Fixed-time synergetic controller for stabilization of hydraulic turbine regulating system," Renewable Energy, Elsevier, vol. 157(C), pages 1233-1242.

    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:eee:energy:v:165:y:2018:i:pb:p:1009-1023. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.