Please use this identifier to cite or link to this item: https://doi.org/10.3390/en14051227
Title: Construction of operational data-driven power curve of a generator by industry 4.0 data analytics
Authors: Ashraf, Waqar Muhammad
Uddin, Ghulam Moeen
Farooq, Muhammad
Riaz, Fahid 
Ahmad, Hassan Afroze
Kamal, Ahmad Hassan
Anwar, Saqib
El-Sherbeeny, Ahmed M.
Khan, Muhammad Haider
Hafeez, Noman
Ali, Arman
Samee, Abdul
Naeem, Muhammad Ahmad
Jamil, Ahsaan
Hassan, Hafiz Ali
Muneeb, Muhammad
Chaudhary, Ijaz Ahmad
Sosnowski, Marcin
Krzywanski, Jaroslaw
Keywords: ANN
AutoML
Generator power
Industry 4.0
Modeling techniques
Operation control
Issue Date: 24-Feb-2021
Publisher: MDPI AG
Citation: Ashraf, Waqar Muhammad, Uddin, Ghulam Moeen, Farooq, Muhammad, Riaz, Fahid, Ahmad, Hassan Afroze, Kamal, Ahmad Hassan, Anwar, Saqib, El-Sherbeeny, Ahmed M., Khan, Muhammad Haider, Hafeez, Noman, Ali, Arman, Samee, Abdul, Naeem, Muhammad Ahmad, Jamil, Ahsaan, Hassan, Hafiz Ali, Muneeb, Muhammad, Chaudhary, Ijaz Ahmad, Sosnowski, Marcin, Krzywanski, Jaroslaw (2021-02-24). Construction of operational data-driven power curve of a generator by industry 4.0 data analytics. Energies 14 (5) : 1227. ScholarBank@NUS Repository. https://doi.org/10.3390/en14051227
Rights: Attribution 4.0 International
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. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Source Title: Energies
URI: https://scholarbank.nus.edu.sg/handle/10635/232124
ISSN: 1996-1073
DOI: 10.3390/en14051227
Rights: Attribution 4.0 International
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