IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v277y2023ics0360544223010356.html
   My bibliography  Save this article

Intelligent fuzzy modeling of heavy-duty gas turbine for smart power generation

Author

Listed:
  • Gong, Linjuan
  • Hou, Guolian
  • Li, Jun
  • Gao, Haidong
  • Gao, Lin
  • Wang, Lin
  • Gao, Yaokui
  • Zhou, Junbo
  • Wang, Mingkun

Abstract

Natural gas-fired combined cycle unit is appropriate alternative of coal-fired unit for clean power generation. To address the dramatic nonlinearity, strong coupling and observable uncertainty of heavy-duty gas turbine, which is deemed as core device in combined cycle unit, a data-driven intelligent fuzzy modeling strategy is proposed for smart power generation. Firstly, the collected on-side data used for model identification is preprocessed through wavelet denoising for data cleaning. Then, an intelligent T-S fuzzy identification method is adopted for plant modeling via identifications of antecedent part and consequence part. In the antecedent part identification, an automatic fuzzy C-means clustering approach is presented to adaptively divide data space under different operation conditions. Besides, the cluster centers modification, and clusters merging are innovatively used to promote rationality of the clustering result for simpler modeling. Furthermore, a simultaneous flower pollination algorithm is proposed to acquire consequence parameters in each sub-model. Finally, the presented intelligent modeling strategy is applied to a heavy-duty gas turbine system in combined cycle unit for simulation validation. In the comparative experiments, fitting errors of the proposed intelligent fuzzy modeling approach are almost half of that of other seven counterparts while corresponding modeling times are at least 1s faster than others. Therefore, the intelligent fuzzy modeling method reveals competitive precise and rapidity.

Suggested Citation

  • Gong, Linjuan & Hou, Guolian & Li, Jun & Gao, Haidong & Gao, Lin & Wang, Lin & Gao, Yaokui & Zhou, Junbo & Wang, Mingkun, 2023. "Intelligent fuzzy modeling of heavy-duty gas turbine for smart power generation," Energy, Elsevier, vol. 277(C).
    • Handle: RePEc:eee:energy:v:277:y:2023:i:c:s0360544223010356
    DOI: 10.1016/j.energy.2023.127641
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223010356
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.127641?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. Zhang, Yuanzhe & Liu, Pei & Li, Zheng, 2023. "Gas turbine off-design behavior modelling and operation windows analysis under different ambient conditions," Energy, Elsevier, vol. 262(PA).
    2. Hou, Guolian & Gong, Linjuan & Hu, Bo & Su, Huilin & Huang, Ting & Huang, Congzhi & Fan, Wei & Zhao, Yuanzhu, 2022. "Application of fast adaptive moth-flame optimization in flexible operation modeling for supercritical unit," Energy, Elsevier, vol. 239(PA).
    3. Pourhedayat, Samira & Hu, Eric & Chen, Lei, 2023. "An improved semi-analytical model for evaluating performance of gas turbine power plants," Energy, Elsevier, vol. 267(C).
    4. Gu, Hui & Zhu, Hongxia & Cui, Xiaobo, 2023. "A modified clustering procedure for energy consumption monitoring in the steam turbine considering volume effect," Energy, Elsevier, vol. 268(C).
    5. Kazemi, Abolghasem & Moreno, Jovita & Iribarren, Diego, 2022. "Techno-economic comparison of optimized natural gas combined cycle power plants with CO2 capture," Energy, Elsevier, vol. 255(C).
    6. El-Dabah, Mahmoud A. & El-Sehiemy, Ragab A. & Hasanien, Hany M. & Saad, Bahaa, 2023. "Photovoltaic model parameters identification using Northern Goshawk Optimization algorithm," Energy, Elsevier, vol. 262(PB).
    7. Yin, Linfei & Wu, Yunzhi, 2022. "Mode-decomposition memory reinforcement network strategy for smart generation control in multi-area power systems containing renewable energy," Applied Energy, Elsevier, vol. 307(C).
    8. Zhu, Mingjuan & Liu, Yudong & Wu, Xiao & Shen, Jiong, 2023. "Dynamic modeling and comprehensive analysis of direct air-cooling coal-fired power plant integrated with carbon capture for reliable, economic and flexible operation," Energy, Elsevier, vol. 263(PA).
    9. Park, Yeseul & Choi, Minsung & Choi, Gyungmin, 2022. "Fault detection of industrial large-scale gas turbine for fuel distribution characteristics in start-up procedure using artificial neural network method," Energy, Elsevier, vol. 251(C).
    10. Otitoju, Olajide & Oko, Eni & Wang, Meihong, 2021. "Technical and economic performance assessment of post-combustion carbon capture using piperazine for large scale natural gas combined cycle power plants through process simulation," Applied Energy, Elsevier, vol. 292(C).
    11. Prasanth Ram, J. & Rajasekar, N., 2017. "A new global maximum power point tracking technique for solar photovoltaic (PV) system under partial shading conditions (PSC)," Energy, Elsevier, vol. 118(C), pages 512-525.
    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. Kazemi, Abolghasem & Moreno, Jovita & Iribarren, Diego, 2023. "Economic optimization and comparative environmental assessment of natural gas combined cycle power plants with CO2 capture," Energy, Elsevier, vol. 277(C).
    2. Hou, Guolian & Huang, Ting & Huang, Congzhi, 2023. "Flexibility improvement of 1000 MW ultra-supercritical unit under full operating conditions by error-based ADRC and fast pigeon-inspired optimizer," Energy, Elsevier, vol. 270(C).
    3. Rezk, Hegazy & AL-Oran, Mazen & Gomaa, Mohamed R. & Tolba, Mohamed A. & Fathy, Ahmed & Abdelkareem, Mohammad Ali & Olabi, A.G. & El-Sayed, Abou Hashema M., 2019. "A novel statistical performance evaluation of most modern optimization-based global MPPT techniques for partially shaded PV system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
    4. Yu-Pei Huang & Cheng-En Ye & Xiang Chen, 2018. "A Modified Firefly Algorithm with Rapid Response Maximum Power Point Tracking for Photovoltaic Systems under Partial Shading Conditions," Energies, MDPI, vol. 11(9), pages 1-33, August.
    5. Zima, Wiesław & Grądziel, Sławomir & Cebula, Artur & Rerak, Monika & Kozak-Jagieła, Ewa & Pilarczyk, Marcin, 2023. "Mathematical model of a power boiler operation under rapid thermal load changes," Energy, Elsevier, vol. 263(PC).
    6. Julio, Alisson Aparecido Vitoriano & Castro-Amoedo, Rafael & Maréchal, François & González, Aldemar Martínez & Escobar Palacio, José Carlos, 2023. "Exergy and economic analysis of the trade-off for design of post-combustion CO2 capture plant by chemical absorption with MEA," Energy, Elsevier, vol. 280(C).
    7. Ju, Liwei & Yin, Zhe & Lu, Xiaolong & Yang, Shenbo & Li, Peng & Rao, Rao & Tan, Zhongfu, 2022. "A Tri-dimensional Equilibrium-based stochastic optimal dispatching model for a novel virtual power plant incorporating carbon Capture, Power-to-Gas and electric vehicle aggregator," Applied Energy, Elsevier, vol. 324(C).
    8. Nawal Rai & Amel Abbadi & Fethia Hamidia & Nadia Douifi & Bdereddin Abdul Samad & Khalid Yahya, 2023. "Biogeography-Based Teaching Learning-Based Optimization Algorithm for Identifying One-Diode, Two-Diode and Three-Diode Models of Photovoltaic Cell and Module," Mathematics, MDPI, vol. 11(8), pages 1-30, April.
    9. Hegazy Rezk & A. G. Olabi & Tabbi Wilberforce & Enas Taha Sayed, 2023. "A Comprehensive Review and Application of Metaheuristics in Solving the Optimal Parameter Identification Problems," Sustainability, MDPI, vol. 15(7), pages 1-24, March.
    10. Ali M. Eltamaly & Hassan M. H. Farh & Mamdooh S. Al Saud, 2019. "Impact of PSO Reinitialization on the Accuracy of Dynamic Global Maximum Power Detection of Variant Partially Shaded PV Systems," Sustainability, MDPI, vol. 11(7), pages 1-14, April.
    11. Fahd A. Alturki & Abdullrahman A. Al-Shamma’a & Hassan M. H. Farh, 2020. "Simulations and dSPACE Real-Time Implementation of Photovoltaic Global Maximum Power Extraction under Partial Shading," Sustainability, MDPI, vol. 12(9), pages 1-16, May.
    12. Al-Momani, Ahmad & Mohamed, Omar & Abu Elhaija, Wejdan, 2022. "Multiple processes modeling and identification for a cleaner supercritical power plant via Grey Wolf Optimizer," Energy, Elsevier, vol. 252(C).
    13. Otitoju, Olajide & Oko, Eni & Wang, Meihong, 2023. "Modelling, scale-up and techno-economic assessment of rotating packed bed absorber for CO2 capture from a 250 MWe combined cycle gas turbine power plant," Applied Energy, Elsevier, vol. 335(C).
    14. Tingting Pei & Xiaohong Hao & Qun Gu, 2018. "A Novel Global Maximum Power Point Tracking Strategy Based on Modified Flower Pollination Algorithm for Photovoltaic Systems under Non-Uniform Irradiation and Temperature Conditions," Energies, MDPI, vol. 11(10), pages 1-16, October.
    15. Yin, Linfei & Li, Yu, 2022. "Hybrid multi-agent emotional deep Q network for generation control of multi-area integrated energy systems," Applied Energy, Elsevier, vol. 324(C).
    16. Prasanth Ram, J. & Rajasekar, N., 2017. "A new robust, mutated and fast tracking LPSO method for solar PV maximum power point tracking under partial shaded conditions," Applied Energy, Elsevier, vol. 201(C), pages 45-59.
    17. Aqiang Zhao & Weimin Wu & Zuoyao Sun & Lixun Zhu & Kaiyuan Lu & Henry Chung & Frede Blaabjerg, 2019. "A Flower Pollination Method Based Global Maximum Power Point Tracking Strategy for Point-Absorbing Type Wave Energy Converters," Energies, MDPI, vol. 12(7), pages 1-19, April.
    18. Zhang, Bin & Wu, Xuewei & Ghias, Amer M.Y.M. & Chen, Zhe, 2023. "Coordinated carbon capture systems and power-to-gas dynamic economic energy dispatch strategy for electricity–gas coupled systems considering system uncertainty: An improved soft actor–critic approach," Energy, Elsevier, vol. 271(C).
    19. Mao, Mingxuan & Zhang, Li & Duan, Pan & Duan, Qichang & Yang, Ming, 2018. "Grid-connected modular PV-Converter system with shuffled frog leaping algorithm based DMPPT controller," Energy, Elsevier, vol. 143(C), pages 181-190.
    20. Shen, Rendong & Zhong, Shengyuan & Wen, Xin & An, Qingsong & Zheng, Ruifan & Li, Yang & Zhao, Jun, 2022. "Multi-agent deep reinforcement learning optimization framework for building energy system with renewable energy," Applied Energy, Elsevier, vol. 312(C).

    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:277:y:2023:i:c:s0360544223010356. 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.