IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i14p3597-d1440275.html
   My bibliography  Save this article

Artificial Neural Network Model for Estimating the Pelton Turbine Shaft Power of a Micro-Hydropower Plant under Different Operating Conditions

Author

Listed:
  • Raúl R. Delgado-Currín

    (Department of Mechanical Engineering, Universidad de La Frontera, Francisco Salazar 01145, Temuco 4811230, Chile
    Department of Mechanical Engineering, Universidad de Chile, Beauchef 851, Santiago 8370456, Chile)

  • Williams R. Calderón-Muñoz

    (Department of Mechanical Engineering, Universidad de Chile, Beauchef 851, Santiago 8370456, Chile
    Center for Sustainable Acceleration of Electromobility-CASE, Universidad de Chile, Beauchef 851, Santiago 8370456, Chile
    Energy Center, Universidad de Chile, Beauchef 851, Santiago 8370456, Chile)

  • J. C. Elicer-Cortés

    (Department of Mechanical Engineering, Universidad de Chile, Beauchef 851, Santiago 8370456, Chile)

Abstract

The optimal performance of a hydroelectric power plant depends on accurate monitoring and well-functioning sensors for data acquisition. This study proposes the use of artificial neural networks (ANNs) to estimate the Pelton turbine shaft power of a 10 kW micro-hydropower plant. In the event of a failure of the sensor measuring the torque and/or rotational speed of the Pelton turbine shaft, the synthetic turbine shaft power data generated by the ANN will allow the turbine output power to be determined. The experimental data were obtained by varying the operating conditions of the micro-hydropower plant, including the variation of the input power to the electric generator and the variation of the injector opening. These changes consequently affected the flow rate and the pressure head at the turbine inlet. The use of artificial neural networks (ANNs) was deemed appropriate due to their ability to model complex relationships between input and output variables. The ANN structure comprised five input variables, fifteen neurons in a hidden layer and an output variable estimating the Pelton turbine power. During the training phase, algorithms such as Levenberg–Marquardt (L–M), Scaled Conjugate Gradient (SCG) and Bayesian were employed. The results indicated an error of 0.39% with L–M and 7% with SCG, with the latter under high-flow and -energy consumption conditions. This study demonstrates the effectiveness of artificial neural networks (ANNs) trained with the Levenberg–Marquardt (L–M) algorithm in estimating turbine shaft power. This contributes to improved performance and decision making in the event of a torque sensor failure.

Suggested Citation

  • Raúl R. Delgado-Currín & Williams R. Calderón-Muñoz & J. C. Elicer-Cortés, 2024. "Artificial Neural Network Model for Estimating the Pelton Turbine Shaft Power of a Micro-Hydropower Plant under Different Operating Conditions," Energies, MDPI, vol. 17(14), pages 1-14, July.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:14:p:3597-:d:1440275
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/14/3597/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/14/3597/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yun Tan & Changshu Zhan & Youchun Pi & Chunhui Zhang & Jinghui Song & Yan Chen & Amir-Mohammad Golmohammadi, 2023. "A Hybrid Algorithm Based on Social Engineering and Artificial Neural Network for Fault Warning Detection in Hydraulic Turbines," Mathematics, MDPI, vol. 11(10), pages 1-18, May.
    2. Telikani, Akbar & Rossi, Mosé & Khajehali, Naghmeh & Renzi, Massimiliano, 2023. "Pumps-as-Turbines’ (PaTs) performance prediction improvement using evolutionary artificial neural networks," Applied Energy, Elsevier, vol. 330(PA).
    3. Morais, Lucas Barros Scianni & Aquila, Giancarlo & de Faria, Victor Augusto Durães & Lima, Luana Medeiros Marangon & Lima, José Wanderley Marangon & de Queiroz, Anderson Rodrigo, 2023. "Short-term load forecasting using neural networks and global climate models: An application to a large-scale electrical power system," Applied Energy, Elsevier, vol. 348(C).
    4. Presas, Alexandre & Valentin, David & Zhao, Weiqiang & Egusquiza, Mònica & Valero, Carme & Egusquiza, Eduard, 2021. "On the use of neural networks for dynamic stress prediction in Francis turbines by means of stationary sensors," Renewable Energy, Elsevier, vol. 170(C), pages 652-660.
    5. Rossi, Mosè & Renzi, Massimiliano, 2018. "A general methodology for performance prediction of pumps-as-turbines using Artificial Neural Networks," Renewable Energy, Elsevier, vol. 128(PA), pages 265-274.
    6. Sun, Haiying & Qiu, Changyu & Lu, Lin & Gao, Xiaoxia & Chen, Jian & Yang, Hongxing, 2020. "Wind turbine power modelling and optimization using artificial neural network with wind field experimental data," Applied Energy, Elsevier, vol. 280(C).
    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. Jiang, Chiju & Zhang, Weihao & Li, Ya & Li, Lele & Wang, Yufan & Huang, Dongming, 2023. "Multi-scale Pix2Pix network for high-fidelity prediction of adiabatic cooling effectiveness in turbine cascade," Energy, Elsevier, vol. 265(C).
    2. Du, Qiuwan & Yang, Like & Li, Liangliang & Liu, Tianyuan & Zhang, Di & Xie, Yonghui, 2022. "Aerodynamic design and optimization of blade end wall profile of turbomachinery based on series convolutional neural network," Energy, Elsevier, vol. 244(PA).
    3. Cheng, Biyi & Du, Jianjun & Yao, Yingxue, 2022. "Machine learning methods to assist structure design and optimization of Dual Darrieus Wind Turbines," Energy, Elsevier, vol. 244(PA).
    4. Zhu, Yongchao & Zhu, Caichao & Tan, Jianjun & Tan, Yong & Rao, Lei, 2022. "Anomaly detection and condition monitoring of wind turbine gearbox based on LSTM-FS and transfer learning," Renewable Energy, Elsevier, vol. 189(C), pages 90-103.
    5. Long, Huan & Xu, Shaohui & Gu, Wei, 2022. "An abnormal wind turbine data cleaning algorithm based on color space conversion and image feature detection," Applied Energy, Elsevier, vol. 311(C).
    6. Zhewei Huang & Yawen Yi, 2024. "Short-Term Load Forecasting for Regional Smart Energy Systems Based on Two-Stage Feature Extraction and Hybrid Inverted Transformer," Sustainability, MDPI, vol. 16(17), pages 1-25, September.
    7. Qinghe Zhao & Xinyi Liu & Junlong Fang, 2023. "Extreme Gradient Boosting Model for Day-Ahead STLF in National Level Power System: Estonia Case Study," Energies, MDPI, vol. 16(24), pages 1-29, December.
    8. Sun, Shilin & Wang, Tianyang & Chu, Fulei, 2022. "In-situ condition monitoring of wind turbine blades: A critical and systematic review of techniques, challenges, and futures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    9. Rossi, Mosè & Nigro, Alessandra & Renzi, Massimiliano, 2019. "Experimental and numerical assessment of a methodology for performance prediction of Pumps-as-Turbines (PaTs) operating in off-design conditions," Applied Energy, Elsevier, vol. 248(C), pages 555-566.
    10. Renzi, Massimiliano & Nigro, Alessandra & Rossi, Mosè, 2020. "A methodology to forecast the main non-dimensional performance parameters of pumps-as-turbines (PaTs) operating at Best Efficiency Point (BEP)," Renewable Energy, Elsevier, vol. 160(C), pages 16-25.
    11. Wang, Yuqi & Du, Qiuwan & Li, Yunzhu & Zhang, Di & Xie, Yonghui, 2022. "Field reconstruction and off-design performance prediction of turbomachinery in energy systems based on deep learning techniques," Energy, Elsevier, vol. 238(PB).
    12. Shiyuan Yang & Hongtao Wang & Yihe Xu & Yongqiang Guo & Lidong Pan & Jiaming Zhang & Xinkai Guo & Debiao Meng & Jiapeng Wang, 2023. "A Coupled Simulated Annealing and Particle Swarm Optimization Reliability-Based Design Optimization Strategy under Hybrid Uncertainties," Mathematics, MDPI, vol. 11(23), pages 1-26, November.
    13. Tavakol Aghaei, Vahid & Ağababaoğlu, Arda & Bawo, Biram & Naseradinmousavi, Peiman & Yıldırım, Sinan & Yeşilyurt, Serhat & Onat, Ahmet, 2023. "Energy optimization of wind turbines via a neural control policy based on reinforcement learning Markov chain Monte Carlo algorithm," Applied Energy, Elsevier, vol. 341(C).
    14. Li, Rui & Zhang, Jincheng & Zhao, Xiaowei, 2022. "Dynamic wind farm wake modeling based on a Bilateral Convolutional Neural Network and high-fidelity LES data," Energy, Elsevier, vol. 258(C).
    15. Li, Jinxing & Liu, Tianyuan & Zhu, Guangya & Li, Yunzhu & Xie, Yonghui, 2023. "Uncertainty quantification and aerodynamic robust optimization of turbomachinery based on graph learning methods," Energy, Elsevier, vol. 273(C).
    16. Purohit, Shantanu & Ng, E.Y.K. & Syed Ahmed Kabir, Ijaz Fazil, 2022. "Evaluation of three potential machine learning algorithms for predicting the velocity and turbulence intensity of a wind turbine wake," Renewable Energy, Elsevier, vol. 184(C), pages 405-420.
    17. Ávila, Carlos Andrés Macías & Sánchez-Romero, Francisco-Javier & López-Jiménez, P. Amparo & Pérez-Sánchez, Modesto, 2021. "Optimization tool to improve the management of the leakages and recovered energy in irrigation water systems," Agricultural Water Management, Elsevier, vol. 258(C).
    18. Crespo Chacón, Miguel & Rodríguez Díaz, Juan Antonio & García Morillo, Jorge & McNabola, Aonghus, 2020. "Hydropower energy recovery in irrigation networks: Validation of a methodology for flow prediction and pump as turbine selection," Renewable Energy, Elsevier, vol. 147(P1), pages 1728-1738.
    19. Venturini, Mauro & Manservigi, Lucrezia & Alvisi, Stefano & Simani, Silvio, 2018. "Development of a physics-based model to predict the performance of pumps as turbines," Applied Energy, Elsevier, vol. 231(C), pages 343-354.
    20. Binama, Maxime & Su, Wen-Tao & Cai, Wei-Hua & Li, Xiao-Bin & Muhirwa, Alexis & Li, Biao & Bisengimana, Emmanuel, 2019. "Blade trailing edge position influencing pump as turbine (PAT) pressure field under part-load conditions," Renewable Energy, Elsevier, vol. 136(C), pages 33-47.

    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:17:y:2024:i:14:p:3597-:d:1440275. 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.