IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i21p7356-d672605.html
   My bibliography  Save this article

Inverse Optimal Control Using Metaheuristics of Hydropower Plant Model via Forecasting Based on the Feature Engineering

Author

Listed:
  • Marlene A. Perez-Villalpando

    (School of Engineering and Technological Innovation, Campus Tonalá, University of Guadalajara, Guadalajara 45425, Jalisco, Mexico)

  • Kelly J. Gurubel Tun

    (School of Engineering and Technological Innovation, Campus Tonalá, University of Guadalajara, Guadalajara 45425, Jalisco, Mexico)

  • Carlos A. Arellano-Muro

    (Western Institute of Technology and Higher Education, Tlaquepaque 45640, Jalisco, Mexico)

  • Fernando Fausto

    (Departamento de Electrónica, Universidad de Guadalajara, Centro Universitario de Ciencias Exactas e Ingenierías, Guadalajara 44430, Jalisco, Mexico)

Abstract

Optimal operation of hydropower plants (HP) is a crucial task for the control of several variables involved in the power generation process, including hydraulic level and power generation rate. In general, there are three main problems that an optimal operation approach must address: (i) maintaining a hydraulic head level which satisfies the energy demand at a given time, (ii) regulating operation to match with certain established conditions, even in the presence of system’s parametric variations, and (iii) managing external disturbances at the system’s input. To address these problems, in this paper we propose an approach for optimal hydraulic level tracking based on an Inverse Optimal Controller (IOC), devised with the purpose of regulating power generation rates on a specific HP infrastructure. The Closed–Loop System (CLS) has been simulated using data collected from the HP through a whole year of operation as a tracking reference. Furthermore, to combat parametric variations, an accumulative action is incorporated into the control scheme. In addition, a Recurrent Neural Network (RNN) based on Feature Engineering (FE) techniques has been implemented to aid the system in the prediction and management of external perturbations. Besides, a landslide is simulated, causing the system’s response to show a deviation in reference tracking, which is corrected through the control action. Afterward, the RNN is including of the aforementioned system, where the trajectories tracking deviation is not perceptible, at the hand of, a better response with respect to use a single scheme. The results show the robustness of the proposed control scheme despite climatic variations and landslides in the reservoir operation process. This proposed combined scheme shows good performance in presence of parametric variations and external perturbations.

Suggested Citation

  • Marlene A. Perez-Villalpando & Kelly J. Gurubel Tun & Carlos A. Arellano-Muro & Fernando Fausto, 2021. "Inverse Optimal Control Using Metaheuristics of Hydropower Plant Model via Forecasting Based on the Feature Engineering," Energies, MDPI, vol. 14(21), pages 1-18, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:21:p:7356-:d:672605
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/21/7356/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/21/7356/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. José D. Morcillo & Fabiola Angulo & Carlos J. Franco, 2020. "Analyzing the Hydroelectricity Variability on Power Markets from a System Dynamics and Dynamic Systems Perspective: Seasonality and ENSO Phenomenon," Energies, MDPI, vol. 13(9), pages 1-25, May.
    2. Tutz, Gerhard & Ramzan, Shahla, 2015. "Improved methods for the imputation of missing data by nearest neighbor methods," Computational Statistics & Data Analysis, Elsevier, vol. 90(C), pages 84-99.
    3. 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.
    4. Emanuele Ogliari & Alfredo Nespoli & Marco Mussetta & Silvia Pretto & Andrea Zimbardo & Nicholas Bonfanti & Manuele Aufiero, 2020. "A Hybrid Method for the Run-Of-The-River Hydroelectric Power Plant Energy Forecast: HYPE Hydrological Model and Neural Network," Forecasting, MDPI, vol. 2(4), pages 1-19, October.
    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. Marek Krok & Paweł Majewski & Wojciech P. Hunek & Tomasz Feliks, 2022. "Energy Optimization of the Continuous-Time Perfect Control Algorithm," Energies, MDPI, vol. 15(4), pages 1-13, February.

    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. Marcos Tadeu Barros de Oliveira & Patrícia de Sousa Oliveira Silva & Elisa Oliveira & André Luís Marques Marcato & Giovani Santiago Junqueira, 2021. "Availability Projections of Hydroelectric Power Plants through Monte Carlo Simulation," Energies, MDPI, vol. 14(24), pages 1-18, December.
    2. Sadaf Kabir & Leily Farrokhvar, 2022. "Non-linear missing data imputation for healthcare data via index-aware autoencoders," Health Care Management Science, Springer, vol. 25(3), pages 484-497, September.
    3. Palanimuthu, Kumarasamy & Joo, Young Hoon, 2023. "Reliability improvement of the large-scale wind turbines with actuator faults using a robust fault-tolerant synergetic pitch control," Renewable Energy, Elsevier, vol. 217(C).
    4. Kanchanaharuthai, Adirak & Mujjalinvimut, Ekkachai, 2022. "Fixed-time command-filtered backstepping control design for hydraulic turbine regulating systems," Renewable Energy, Elsevier, vol. 184(C), pages 1091-1103.
    5. He Wang & Zhijie Ma, 2021. "Regulation Characteristics and Load Optimization of Pump-Turbine in Variable-Speed Operation," Energies, MDPI, vol. 14(24), pages 1-21, December.
    6. Angela Gorgoglione & Alberto Castro & Christian Chreties & Lorena Etcheverry, 2020. "Overcoming Data Scarcity in Earth Science," Data, MDPI, vol. 5(1), pages 1-5, January.
    7. Faisal Shahla & Tutz Gerhard, 2017. "Missing value imputation for gene expression data by tailored nearest neighbors," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 16(2), pages 95-106, April.
    8. Zapata, Sebastian & Castaneda, Monica & Herrera, Milton M. & Dyner, Isaac, 2023. "Investigating the concurrence of transmission grid expansion and the dissemination of renewables," Energy, Elsevier, vol. 276(C).
    9. José D. Morcillo & Fabiola Angulo & Carlos J. Franco, 2021. "Simulation and Analysis of Renewable and Nonrenewable Capacity Scenarios under Hybrid Modeling: A Case Study," Mathematics, MDPI, vol. 9(13), pages 1-26, July.

    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:14:y:2021:i:21:p:7356-:d:672605. 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.