Please use this identifier to cite or link to this item: https://doi.org/10.3390/en14051227
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dc.titleConstruction of operational data-driven power curve of a generator by industry 4.0 data analytics
dc.contributor.authorAshraf, Waqar Muhammad
dc.contributor.authorUddin, Ghulam Moeen
dc.contributor.authorFarooq, Muhammad
dc.contributor.authorRiaz, Fahid
dc.contributor.authorAhmad, Hassan Afroze
dc.contributor.authorKamal, Ahmad Hassan
dc.contributor.authorAnwar, Saqib
dc.contributor.authorEl-Sherbeeny, Ahmed M.
dc.contributor.authorKhan, Muhammad Haider
dc.contributor.authorHafeez, Noman
dc.contributor.authorAli, Arman
dc.contributor.authorSamee, Abdul
dc.contributor.authorNaeem, Muhammad Ahmad
dc.contributor.authorJamil, Ahsaan
dc.contributor.authorHassan, Hafiz Ali
dc.contributor.authorMuneeb, Muhammad
dc.contributor.authorChaudhary, Ijaz Ahmad
dc.contributor.authorSosnowski, Marcin
dc.contributor.authorKrzywanski, Jaroslaw
dc.date.accessioned2022-10-11T08:01:39Z
dc.date.available2022-10-11T08:01:39Z
dc.date.issued2021-02-24
dc.identifier.citationAshraf, 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
dc.identifier.issn1996-1073
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/232124
dc.description.abstractConstructing 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.
dc.publisherMDPI AG
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourceScopus OA2021
dc.subjectANN
dc.subjectAutoML
dc.subjectGenerator power
dc.subjectIndustry 4.0
dc.subjectModeling techniques
dc.subjectOperation control
dc.typeArticle
dc.contributor.departmentDEPT OF MECHANICAL ENGINEERING
dc.description.doi10.3390/en14051227
dc.description.sourcetitleEnergies
dc.description.volume14
dc.description.issue5
dc.description.page1227
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