IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v285y2023ics0360544223027251.html
   My bibliography  Save this article

Application of deep learning modelling of the optimal operation conditions of auxiliary equipment of combined cycle gas turbine power station

Author

Listed:
  • Chen, Youliang
  • Huang, Xiaoguang
  • Li, Wei
  • Fan, Rong
  • Zi, Pingyang
  • Wang, Xin

Abstract

Reasonable condition adjustment of the auxiliary machinery can optimize the operation performance of the Combined Cycle Gas Turbine (CCGT) power station. In this paper an optimization method for the operation control of the auxiliary equipment based on deep learning is proposed and applied to an F-class gas turbine generation unit. A database of the optimal operating conditions is established to achieve the best economic benefit based on the analysis of two years of historical operation data. The control optimization models are built using the machine learning algorithms including the Multi-Layer Perceptron (MLP), Supporting Vector Machine (SVM), Gaussian process regression and linear regression methods and their accuracy has been compared. The input parameters of these models consist of the load rate, the heating output and the ambient temperature, with the output parameters including the electrical currents for the mechanical draft cooling tower, the high pressure feed pump, the medium pressure feed pump and the condensate pump. The MLP model is designed with different network hidden layer structures and provides the highest calculation accuracy with the least average error of 1.82 %. Different training data sizes are compared and the optimal control trajectory is analyzed. The result can provide useful references for the optimization of auxiliary equipment operations.

Suggested Citation

  • Chen, Youliang & Huang, Xiaoguang & Li, Wei & Fan, Rong & Zi, Pingyang & Wang, Xin, 2023. "Application of deep learning modelling of the optimal operation conditions of auxiliary equipment of combined cycle gas turbine power station," Energy, Elsevier, vol. 285(C).
    • Handle: RePEc:eee:energy:v:285:y:2023:i:c:s0360544223027251
    DOI: 10.1016/j.energy.2023.129331
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223027251
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.129331?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. Colpier, Ulrika Claeson & Cornland, Deborah, 2002. "The economics of the combined cycle gas turbine--an experience curve analysis," Energy Policy, Elsevier, vol. 30(4), pages 309-316, March.
    2. Kotowicz, Janusz & Brzęczek, Mateusz, 2018. "Analysis of increasing efficiency of modern combined cycle power plant: A case study," Energy, Elsevier, vol. 153(C), pages 90-99.
    3. Zhang, Yachao & Huang, Zhanghao & Zheng, Feng & Zhou, Rongyu & Le, Jian & An, Xueli, 2020. "Cooperative optimization scheduling of the electricity-gas coupled system considering wind power uncertainty via a decomposition-coordination framework," Energy, Elsevier, vol. 194(C).
    4. Faridpak, Behdad & Farrokhifar, Meisam & Murzakhanov, Ilgiz & Safari, Amin, 2020. "A series multi-step approach for operation Co-optimization of integrated power and natural gas systems," Energy, Elsevier, vol. 204(C).
    5. Jesus L. Lobo & Igor Ballesteros & Izaskun Oregi & Javier Del Ser & Sancho Salcedo-Sanz, 2020. "Stream Learning in Energy IoT Systems: A Case Study in Combined Cycle Power Plants," Energies, MDPI, vol. 13(3), pages 1-28, February.
    6. Seme, Sebastijan & Sredenšek, Klemen & Praunseis, Zdravko & Štumberger, Bojan & Hadžiselimović, Miralem, 2018. "Optimal price of electricity of solar power plants and small hydro power plants – Technical and economical part of investments," Energy, Elsevier, vol. 157(C), pages 87-95.
    7. Kim, Sangjo, 2021. "A new performance adaptation method for aero gas turbine engines based on large amounts of measured data," Energy, Elsevier, vol. 221(C).
    8. Fangfang Zheng & Xiaofang Meng & Lidi Wang & Nannan Zhang, 2023. "Operation Optimization Method of Distribution Network with Wind Turbine and Photovoltaic Considering Clustering and Energy Storage," Sustainability, MDPI, vol. 15(3), pages 1-22, January.
    9. Teichgraeber, Holger & Brandt, Adam R., 2019. "Clustering methods to find representative periods for the optimization of energy systems: An initial framework and comparison," Applied Energy, Elsevier, vol. 239(C), pages 1283-1293.
    10. Luo, Xianglong & Huang, Xiaojian & El-Halwagi, Mahmoud M. & Ponce-Ortega, José María & Chen, Ying, 2016. "Simultaneous synthesis of utility system and heat exchanger network incorporating steam condensate and boiler feedwater," Energy, Elsevier, vol. 113(C), pages 875-893.
    11. Wogrin, Sonja & Galbally, David & Ramos, Andrés, 2016. "CCGT unit commitment model with first-principle formulation of cycling costs due to fatigue damage," Energy, Elsevier, vol. 113(C), pages 227-247.
    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. Tang, Zhenhao & Sui, Mengxuan & Wang, Xu & Xue, Wenyuan & Yang, Yuan & Wang, Zhi & Ouyang, Tinghui, 2024. "Theory-guided deep neural network for boiler 3-D NOx concentration distribution prediction," Energy, Elsevier, vol. 299(C).
    2. Wang, Runchen & Du, Xiaonan & Shi, Yuetao & Deng, Weipeng & Wang, Yuhao & Sun, Fengzhong, 2024. "A novel system for reducing power plant electricity consumption and enhancing deep peak-load capability," Energy, Elsevier, vol. 295(C).

    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. Jun Dong & Peiwen Yang & Shilin Nie, 2019. "Day-Ahead Scheduling Model of the Distributed Small Hydro-Wind-Energy Storage Power System Based on Two-Stage Stochastic Robust Optimization," Sustainability, MDPI, vol. 11(10), pages 1-27, May.
    2. Kenjiro Yagi & Ramteen Sioshansi, 2023. "Simplifying capacity planning for electricity systems with hydroelectric and renewable generation," Computational Management Science, Springer, vol. 20(1), pages 1-28, December.
    3. Lafond, François & Bailey, Aimee Gotway & Bakker, Jan David & Rebois, Dylan & Zadourian, Rubina & McSharry, Patrick & Farmer, J. Doyne, 2018. "How well do experience curves predict technological progress? A method for making distributional forecasts," Technological Forecasting and Social Change, Elsevier, vol. 128(C), pages 104-117.
    4. Ju-Yeol Ryu & Sungho Park & Changhyeong Lee & Seonghyeon Hwang & Jongwoong Lim, 2023. "Techno-Economic Analysis of Hydrogen–Natural Gas Blended Fuels for 400 MW Combined Cycle Power Plants (CCPPs)," Energies, MDPI, vol. 16(19), pages 1-19, September.
    5. Merrick, James H. & Bistline, John E.T. & Blanford, Geoffrey J., 2024. "On representation of energy storage in electricity planning models," Energy Economics, Elsevier, vol. 136(C).
    6. Kotowicz, Janusz & Brzęczek, Mateusz, 2019. "Comprehensive multivariable analysis of the possibility of an increase in the electrical efficiency of a modern combined cycle power plant with and without a CO2 capture and compression installations ," Energy, Elsevier, vol. 175(C), pages 1100-1120.
    7. Lee, Shun-Chung & Shih, Li-Hsing, 2010. "Renewable energy policy evaluation using real option model -- The case of Taiwan," Energy Economics, Elsevier, vol. 32(Supplemen), pages 67-78, September.
    8. Shen, Feifei & Zhao, Liang & Du, Wenli & Zhong, Weimin & Qian, Feng, 2020. "Large-scale industrial energy systems optimization under uncertainty: A data-driven robust optimization approach," Applied Energy, Elsevier, vol. 259(C).
    9. Pettersson, Fredrik, 2007. "Carbon pricing and the diffusion of renewable power generation in Eastern Europe: A linear programming approach," Energy Policy, Elsevier, vol. 35(4), pages 2412-2425, April.
    10. Xiao, Wu & Cheng, Andi & Li, Shuai & Jiang, Xiaobin & Ruan, Xuehua & He, Gaohong, 2021. "A multi-objective optimization strategy of steam power system to achieve standard emission and optimal economic by NSGA-Ⅱ," Energy, Elsevier, vol. 232(C).
    11. Taler, Jan & Taler, Dawid & Kaczmarski, Karol & Dzierwa, Piotr & Trojan, Marcin & Sobota, Tomasz, 2018. "Monitoring of thermal stresses in pressure components based on the wall temperature measurement," Energy, Elsevier, vol. 160(C), pages 500-519.
    12. Hoffmann, Maximilian & Priesmann, Jan & Nolting, Lars & Praktiknjo, Aaron & Kotzur, Leander & Stolten, Detlef, 2021. "Typical periods or typical time steps? A multi-model analysis to determine the optimal temporal aggregation for energy system models," Applied Energy, Elsevier, vol. 304(C).
    13. Dranka, Géremi Gilson & Ferreira, Paula & Vaz, A. Ismael F., 2021. "A review of co-optimization approaches for operational and planning problems in the energy sector," Applied Energy, Elsevier, vol. 304(C).
    14. Leisen, Robin & Radek, Julian & Weber, Christoph, 2024. "Modeling combined-cycle power plants in a detailed electricity market model," Energy, Elsevier, vol. 298(C).
    15. Chang, Chenglin & Chen, Xiaolu & Wang, Yufei & Feng, Xiao, 2017. "Simultaneous optimization of multi-plant heat integration using intermediate fluid circles," Energy, Elsevier, vol. 121(C), pages 306-317.
    16. Zirngast, Klavdija & Kravanja, Zdravko & Novak Pintarič, Zorka, 2021. "An improved algorithm for synthesis of heat exchanger network with a large number of uncertain parameters," Energy, Elsevier, vol. 233(C).
    17. Nelson, Sarah & Allwood, Julian M., 2021. "The technological and social timelines of climate mitigation: Lessons from 12 past transitions," Energy Policy, Elsevier, vol. 152(C).
    18. Boldyryev, Stanislav & Shamraev, Anatoly A. & Shamraeva, Elena O., 2021. "The design of the total site exchanger network with intermediate heat carriers: Theoretical insights and practical application," Energy, Elsevier, vol. 223(C).
    19. Zhang, Yachao & Liu, Wei & Huang, Zhanghao & Zheng, Feng & Le, Jian & Zhu, Shu, 2021. "Distributionally robust coordination optimization scheduling for electricity-gas-transportation coupled system considering multiple uncertainties," Renewable Energy, Elsevier, vol. 163(C), pages 2037-2052.
    20. Angerer, Michael & Kahlert, Steffen & Spliethoff, Hartmut, 2017. "Transient simulation and fatigue evaluation of fast gas turbine startups and shutdowns in a combined cycle plant with an innovative thermal buffer storage," Energy, Elsevier, vol. 130(C), pages 246-257.

    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:285:y:2023:i:c:s0360544223027251. 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.