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Construction of Operational Data-Driven Power Curve of a Generator by Industry 4.0 Data Analytics

Author

Listed:
  • Waqar Muhammad Ashraf

    (Huaneng Shandong Ruyi (Pakistan) Energy Pvt. Ltd. Sahiwal Coal Power Complex, Sahiwal Punjab 57000, Pakistan
    Department of Mechanical Engineering, University of Engineering and Technology, Lahore Punjab 54890, Pakistan)

  • Ghulam Moeen Uddin

    (Department of Mechanical Engineering, University of Engineering and Technology, Lahore Punjab 54890, Pakistan)

  • Muhammad Farooq

    (Department of Mechanical Engineering, University of Engineering and Technology, Lahore Punjab 54890, Pakistan)

  • Fahid Riaz

    (Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore 117575, Singapore)

  • Hassan Afroze Ahmad

    (Huaneng Shandong Ruyi (Pakistan) Energy Pvt. Ltd. Sahiwal Coal Power Complex, Sahiwal Punjab 57000, Pakistan)

  • Ahmad Hassan Kamal

    (Huaneng Shandong Ruyi (Pakistan) Energy Pvt. Ltd. Sahiwal Coal Power Complex, Sahiwal Punjab 57000, Pakistan
    School of Mechanical & Manufacturing Engineering, NUST H-12, Islamabad 44000, Pakistan)

  • Saqib Anwar

    (Industrial Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia)

  • Ahmed M. El-Sherbeeny

    (Industrial Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia)

  • Muhammad Haider Khan

    (Institute of Energy & Environment Engineering, University of the Punjab Lahore, Punjab 54000, Pakistan)

  • Noman Hafeez

    (Department of Computer Science Government College, University Lahore, Punjab 54000, Pakistan)

  • Arman Ali

    (Huaneng Shandong Ruyi (Pakistan) Energy Pvt. Ltd. Sahiwal Coal Power Complex, Sahiwal Punjab 57000, Pakistan)

  • Abdul Samee

    (Department of Mechanical Engineering, University of Engineering and Technology, Lahore Punjab 54890, Pakistan)

  • Muhammad Ahmad Naeem

    (Department of Mechanical Engineering Technology, Punjab Tianjin University of Technology, Lahore 54000, Pakistan)

  • Ahsaan Jamil

    (Huaneng Shandong Ruyi (Pakistan) Energy Pvt. Ltd. Sahiwal Coal Power Complex, Sahiwal Punjab 57000, Pakistan)

  • Hafiz Ali Hassan

    (Huaneng Shandong Ruyi (Pakistan) Energy Pvt. Ltd. Sahiwal Coal Power Complex, Sahiwal Punjab 57000, Pakistan
    Department of Mechanical Engineering, University of Engineering and Technology, Lahore Punjab 54890, Pakistan)

  • Muhammad Muneeb

    (Huaneng Shandong Ruyi (Pakistan) Energy Pvt. Ltd. Sahiwal Coal Power Complex, Sahiwal Punjab 57000, Pakistan
    Department of Mechanical Engineering, University of Engineering and Technology, Lahore Punjab 54890, Pakistan)

  • Ijaz Ahmad Chaudhary

    (Department of Industrial Engineering, University of Management and Technology, Lahore, Punjab 54770, Pakistan)

  • Marcin Sosnowski

    (Faculty of Science and Technology, Jan Dlugosz University in Czestochowa, Armii Krajowej 13/15, 42-200 Czestochowa, Poland)

  • Jaroslaw Krzywanski

    (Faculty of Science and Technology, Jan Dlugosz University in Czestochowa, Armii Krajowej 13/15, 42-200 Czestochowa, Poland)

Abstract

Constructing the power curve of a power generation facility integrated with complex and large-scale industrial processes is a difficult task but can be accomplished using Industry 4.0 data analytics tools. This research attempts to construct the data-driven power curve of the generator installed at a 660 MW power plant by incorporating artificial intelligence (AI)-based modeling tools. The power produced from the generator is modeled by an artificial neural network (ANN)—a reliable data analytical technique of deep learning. Similarly, the R2.ai application, which belongs to the automated machine learning (AutoML) platform, is employed to show the alternative modeling methods in using the AI approach. Comparatively, the ANN performed well in the external validation test and was deployed to construct the generator’s power curve. Monte Carlo experiments comprising the power plant’s thermo-electric operating parameters and the Gaussian noise are simulated with the ANN, and thus the power curve of the generator is constructed with a 95% confidence interval. The performance curves of industrial systems and machinery based on their operational data can be constructed using ANNs, and the decisions driven by these performance curves could contribute to the Industry 4.0 vision of effective operation management.

Suggested Citation

  • Waqar Muhammad Ashraf & Ghulam Moeen Uddin & Muhammad Farooq & Fahid Riaz & Hassan Afroze Ahmad & Ahmad Hassan Kamal & Saqib Anwar & Ahmed M. El-Sherbeeny & Muhammad Haider Khan & Noman Hafeez & Arman, 2021. "Construction of Operational Data-Driven Power Curve of a Generator by Industry 4.0 Data Analytics," Energies, MDPI, vol. 14(5), pages 1-18, February.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:5:p:1227-:d:504832
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    References listed on IDEAS

    as
    1. Liu, Zuming & Karimi, Iftekhar A., 2020. "Gas turbine performance prediction via machine learning," Energy, Elsevier, vol. 192(C).
    2. Waqar Muhammad Ashraf & Ghulam Moeen Uddin & Syed Muhammad Arafat & Sher Afghan & Ahmad Hassan Kamal & Muhammad Asim & Muhammad Haider Khan & Muhammad Waqas Rafique & Uwe Naumann & Sajawal Gul Niazi &, 2020. "Optimization of a 660 MW e Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management Part 1. Thermal Efficiency," Energies, MDPI, vol. 13(21), pages 1-33, October.
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