IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i20p3903-d276731.html
   My bibliography  Save this article

Accurate Parameter Estimation of a Hydro-Turbine Regulation System Using Adaptive Fuzzy Particle Swarm Optimization

Author

Listed:
  • Dong Liu

    (Key Laboratory of Hydraulic Machinery Transients, Ministry of Education, Wuhan University, Wuhan 430072, China
    School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China)

  • Zhihuai Xiao

    (Key Laboratory of Hydraulic Machinery Transients, Ministry of Education, Wuhan University, Wuhan 430072, China
    School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China)

  • Hongtao Li

    (Preparatory Office of Wudongde Hydropower Plant, China Yangtze Power Co., Ltd., Kunming 650000, China)

  • Dong Liu

    (State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China)

  • Xiao Hu

    (Key Laboratory of Hydraulic Machinery Transients, Ministry of Education, Wuhan University, Wuhan 430072, China
    School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China)

  • O.P. Malik

    (Department of Electrical and Computer Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada)

Abstract

Parameter estimation is an important part in the modeling of a hydro-turbine regulation system (HTRS), and the results determine the final accuracy of a model. A hydro-turbine is normally a non-minimum phase system with strong nonlinearity and time-varying parameters. For the parameter estimation of such a nonlinear system, heuristic algorithms are more advantageous than traditional mathematical methods. However, most heuristics based algorithms and their improved versions are not adaptive, which means that the appropriate parameters of an algorithm need to be manually found to keep the algorithm performing optimally in solving similar problems. To solve this problem, an adaptive fuzzy particle swarm optimization (AFPSO) algorithm that dynamically tunes the parameters according to model error is proposed and applied to the parameter estimation of the HTRS. The simulation studies show that the proposed AFPSO contributes to lower model error and higher identification accuracy compared with some traditional heuristic algorithms. Importantly, it avoids a possible deterioration in the performance of an algorithm caused by inappropriate parameter selection.

Suggested Citation

  • Dong Liu & Zhihuai Xiao & Hongtao Li & Dong Liu & Xiao Hu & O.P. Malik, 2019. "Accurate Parameter Estimation of a Hydro-Turbine Regulation System Using Adaptive Fuzzy Particle Swarm Optimization," Energies, MDPI, vol. 12(20), pages 1-21, October.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:20:p:3903-:d:276731
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/20/3903/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/20/3903/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yu, Shiwei & Wei, Yi-Ming & Wang, Ke, 2012. "A PSO–GA optimal model to estimate primary energy demand of China," Energy Policy, Elsevier, vol. 42(C), pages 329-340.
    2. Feng, Zhong-kai & Niu, Wen-jing & Cheng, Chun-tian, 2017. "Multi-objective quantum-behaved particle swarm optimization for economic environmental hydrothermal energy system scheduling," Energy, Elsevier, vol. 131(C), pages 165-178.
    3. Agoston E. Eiben & Jim Smith, 2015. "From evolutionary computation to the evolution of things," Nature, Nature, vol. 521(7553), pages 476-482, May.
    4. H. Eduardo Ariza Chacón & Edison Banguero & Antonio Correcher & Ángel Pérez-Navarro & Francisco Morant, 2018. "Modelling, Parameter Identification, and Experimental Validation of a Lead Acid Battery Bank Using Evolutionary Algorithms," Energies, MDPI, vol. 11(9), pages 1-14, September.
    5. Kishor, Nand & Saini, R.P. & Singh, S.P., 2007. "A review on hydropower plant models and control," Renewable and Sustainable Energy Reviews, Elsevier, vol. 11(5), pages 776-796, June.
    6. Yu Huang & Feng Guo & Yongling Li & Yufeng Liu, 2015. "Parameter Estimation of Fractional-Order Chaotic Systems by Using Quantum Parallel Particle Swarm Optimization Algorithm," PLOS ONE, Public Library of Science, vol. 10(1), pages 1-14, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Liu, Dong & Li, Chaoshun & Malik, O.P., 2021. "Nonlinear modeling and multi-scale damping characteristics of hydro-turbine regulation systems under complex variable hydraulic and electrical network structures," Applied Energy, Elsevier, vol. 293(C).
    2. Lei Chen & Bingjie Zhao & Yunpeng Ma, 2023. "FSSSA: A Fuzzy Squirrel Search Algorithm Based on Wide-Area Search for Numerical and Engineering Optimization Problems," Mathematics, MDPI, vol. 11(17), pages 1-42, August.
    3. Marcin Drzewiecki & Jarosław Guziński, 2020. "Fuzzy Control of Waves Generation in a Towing Tank," Energies, MDPI, vol. 13(8), pages 1-17, April.
    4. Liu, Dong & Li, Chaoshun & Tan, Xiaoqiang & Lu, Xueding & Malik, O.P., 2021. "Damping characteristics analysis of hydropower units under full operating conditions and control parameters: Accurate quantitative evaluation based on refined models," Applied Energy, Elsevier, vol. 292(C).
    5. Liu, Dong & Wang, Xin & Peng, Yunshui & Zhang, Hui & Xiao, Zhihuai & Han, Xiangdong & Malik, O.P., 2020. "Stability analysis of hydropower units under full operating conditions considering turbine nonlinearity," Renewable Energy, Elsevier, vol. 154(C), pages 723-742.

    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. Yi, Choong-Sung & Lee, Jin-Hee & Shim, Myung-Pil, 2010. "Site location analysis for small hydropower using geo-spatial information system," Renewable Energy, Elsevier, vol. 35(4), pages 852-861.
    2. Guo Li & Wenling Liu & Zhaohua Wang & Mengqi Liu, 2017. "An empirical examination of energy consumption, behavioral intention, and situational factors: evidence from Beijing," Annals of Operations Research, Springer, vol. 255(1), pages 507-524, August.
    3. Suberu, Mohammed Yekini & Mustafa, Mohd Wazir & Bashir, Nouruddeen & Muhamad, Nor Asiah & Mokhtar, Ahmad Safawi, 2013. "Power sector renewable energy integration for expanding access to electricity in sub-Saharan Africa," Renewable and Sustainable Energy Reviews, Elsevier, vol. 25(C), pages 630-642.
    4. Zhen-Yao Chen & R. J. Kuo, 2019. "Combining SOM and evolutionary computation algorithms for RBF neural network training," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1137-1154, March.
    5. Hasan Akbari & Juan I. Pérez-Díaz & José-Ignacio Sarasúa & Robert Schürhuber, 2023. "Implementation and Evaluation of a Complex Pumped-Storage Hydropower Plant with Four Units, Common Penstock, and Surge Tank in a Real-Time Digital Simulator," Energies, MDPI, vol. 16(9), pages 1-23, April.
    6. Carlos A. Platero & José A. Sánchez & Christophe Nicolet & Philippe Allenbach, 2019. "Hydropower Plants Frequency Regulation Depending on Upper Reservoir Water Level," Energies, MDPI, vol. 12(9), pages 1-15, April.
    7. Askarzadeh, Alireza, 2014. "Comparison of particle swarm optimization and other metaheuristics on electricity demand estimation: A case study of Iran," Energy, Elsevier, vol. 72(C), pages 484-491.
    8. Salhi, Issam & Doubabi, Saïd & Essounbouli, Najib & Hamzaoui, Abdelaziz, 2010. "Application of multi-model control with fuzzy switching to a micro hydro-electrical power plant," Renewable Energy, Elsevier, vol. 35(9), pages 2071-2079.
    9. Xu Tang & Benjamin C. McLellan & Simon Snowden & Baosheng Zhang & Mikael Höök, 2015. "Dilemmas for China: Energy, Economy and Environment," Sustainability, MDPI, vol. 7(5), pages 1-13, May.
    10. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
    11. Li, Chao & Zhai, Rongrong & Yang, Yongping & Patchigolla, Kumar & Oakey, John E. & Turner, Peter, 2019. "Annual performance analysis and optimization of a solar tower aided coal-fired power plant," Applied Energy, Elsevier, vol. 237(C), pages 440-456.
    12. Si, Yupeng & Wang, Rongjie & Zhang, Shiqi & Zhou, Wenting & Lin, Anhui & Zeng, Guangmiao, 2022. "Configuration optimization and energy management of hybrid energy system for marine using quantum computing," Energy, Elsevier, vol. 253(C).
    13. Shuang Li & Yong Yang & Qing Xia, 2018. "Dynamic Safety Assessment in Nonlinear Hydropower Generation Systems," Complexity, Hindawi, vol. 2018, pages 1-8, April.
    14. Vinod, J. & Sarkar, Bikash K. & Sanyal, Dipankar, 2022. "Flow control in a small Francis turbine by system identification and fuzzy adaptation of PID and deadband controllers," Renewable Energy, Elsevier, vol. 201(P2), pages 87-99.
    15. Manuel Chica & Joaquín Bautista & Jesica de Armas, 2019. "Benefits of robust multiobjective optimization for flexible automotive assembly line balancing," Flexible Services and Manufacturing Journal, Springer, vol. 31(1), pages 75-103, March.
    16. Xin-gang, Zhao & Ze-qi, Zhang & Yi-min, Xie & Jin, Meng, 2020. "Economic-environmental dispatch of microgrid based on improved quantum particle swarm optimization," Energy, Elsevier, vol. 195(C).
    17. Jiandong Yang & Mingjiang Wang & Chao Wang & Wencheng Guo, 2015. "Linear Modeling and Regulation Quality Analysis for Hydro-Turbine Governing System with an Open Tailrace Channel," Energies, MDPI, vol. 8(10), pages 1-16, October.
    18. Geon Lee & Hyunjung Kang & Jooyeong Yun & Dongwoo Chae & Minsu Jeong & Minseo Jeong & Dasol Lee & Miso Kim & Heon Lee & Junsuk Rho, 2024. "Integrated triboelectric nanogenerator and radiative cooler for all-weather transparent glass surfaces," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    19. Ma, Weimin & Zhu, Xiaoxi & Wang, Miaomiao, 2013. "Forecasting iron ore import and consumption of China using grey model optimized by particle swarm optimization algorithm," Resources Policy, Elsevier, vol. 38(4), pages 613-620.
    20. Luciano Ferreira Cruz & Flavia Bernardo Pinto & Lucas Camilotti & Angelo Marcio Oliveira Santanna & Roberto Zanetti Freire & Leandro Santos Coelho, 2022. "Improved multiobjective differential evolution with spherical pruning algorithm for optimizing 3D printing technology parametrization process," Annals of Operations Research, Springer, vol. 319(2), pages 1565-1587, December.

    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:jeners:v:12:y:2019:i:20:p:3903-:d:276731. 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.