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

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 Ali, Abdul Samee, Muhammad Ahmad Naeem, Ahsaan Jamil, Hafiz Ali Hassan, Muhammad Muneeb, Ijaz Ahmad Chaudhary, Marcin Sosnowski and Jaroslaw Krzywanski
Additional contact information
Waqar Muhammad Ashraf: Huaneng Shandong Ruyi (Pakistan) Energy Pvt. Ltd. Sahiwal Coal Power Complex, Sahiwal Punjab 57000, 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
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
Muhammad Muneeb: Huaneng Shandong Ruyi (Pakistan) Energy Pvt. Ltd. Sahiwal Coal Power Complex, Sahiwal Punjab 57000, 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

Energies, 2021, vol. 14, issue 5, 1-18

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.

Keywords: generator power; modeling techniques; operation control; ANN; AutoML; Industry 4.0 (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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