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; Jaroslaw Krzywanski

LAPSE

Living Archive for Process Systems Engineering

LAPSE:2023.29895

Published Article

LAPSE:2023.29895

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, Jaroslaw Krzywanski

April 14, 2023

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.

Record ID

LAPSE:2023.29895

Keywords

ANN, AutoML, generator power, Industry 4.0, modeling techniques, operation control

Subject

Numerical Methods and Statistics

Suggested Citation

Ashraf WM, Uddin GM, Farooq M, Riaz F, Ahmad HA, Kamal AH, Anwar S, El-Sherbeeny AM, Khan MH, Hafeez N, Ali A, Samee A, Naeem MA, Jamil A, Hassan HA, Muneeb M, Chaudhary IA, Sosnowski M, Krzywanski J. Construction of Operational Data-Driven Power Curve of a Generator by Industry 4.0 Data Analytics. (2023). LAPSE:2023.29895

Author Affiliations

Ashraf WM: 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 [ORCID]
Uddin GM: Department of Mechanical Engineering, University of Engineering and Technology, Lahore Punjab 54890, Pakistan
Farooq M: Department of Mechanical Engineering, University of Engineering and Technology, Lahore Punjab 54890, Pakistan [ORCID]
Riaz F: Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore 117575, Singapore [ORCID]
Ahmad HA: Huaneng Shandong Ruyi (Pakistan) Energy Pvt. Ltd. Sahiwal Coal Power Complex, Sahiwal Punjab 57000, Pakistan
Kamal AH: 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
Anwar S: Industrial Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia [ORCID]
El-Sherbeeny AM: Industrial Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia [ORCID]
Khan MH: Institute of Energy & Environment Engineering, University of the Punjab Lahore, Punjab 54000, Pakistan
Hafeez N: Department of Computer Science Government College, University Lahore, Punjab 54000, Pakistan
Ali A: Huaneng Shandong Ruyi (Pakistan) Energy Pvt. Ltd. Sahiwal Coal Power Complex, Sahiwal Punjab 57000, Pakistan
Samee A: Department of Mechanical Engineering, University of Engineering and Technology, Lahore Punjab 54890, Pakistan
Naeem MA: Department of Mechanical Engineering Technology, Punjab Tianjin University of Technology, Lahore 54000, Pakistan
Jamil A: Huaneng Shandong Ruyi (Pakistan) Energy Pvt. Ltd. Sahiwal Coal Power Complex, Sahiwal Punjab 57000, Pakistan
Hassan HA: 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
Muneeb M: 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
Chaudhary IA: Department of Industrial Engineering, University of Management and Technology, Lahore, Punjab 54770, Pakistan
Sosnowski M: Faculty of Science and Technology, Jan Dlugosz University in Czestochowa, Armii Krajowej 13/15, 42-200 Czestochowa, Poland [ORCID]
Krzywanski J: Faculty of Science and Technology, Jan Dlugosz University in Czestochowa, Armii Krajowej 13/15, 42-200 Czestochowa, Poland [ORCID]

Journal Name

Energies

Volume

14

Issue

5

First Page

1227

Year

2021

Publication Date

2021-02-24