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Predicting the performance of electrical machines using machine learning

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dc.contributor.author Manohar, VJ
dc.contributor.author Jha, SK
dc.contributor.editor Piyatilake, ITS
dc.contributor.editor Thalagala, PD
dc.contributor.editor Ganegoda, GU
dc.contributor.editor Thanuja, ALARR
dc.contributor.editor Dharmarathna, P
dc.date.accessioned 2024-02-06T09:22:23Z
dc.date.available 2024-02-06T09:22:23Z
dc.date.issued 2023-12-07
dc.identifier.uri http://dl.lib.uom.lk/handle/123/22200
dc.description.abstract Electrical machines play an important role in our day-to-day life. Electric machines like DC motors and 3- phase induction motors are essential systems and widely used in domestic, industrial and transportation systems. In order to operate the machines optimally and efficiently, in real time operations, it is required to predict the performance parameters at various loaded conditions. With the advancements in the field of predictive modelling and analytics, several researchers have applied in the area of energy consumption prediction, fault prediction, weather prediction, power grid management and so on. In this paper, the machine learning techniques are demonstrated that may be used to examine the performance of electrical machinery by forecasting performance characteristics like speed and efficiency. To validate the performance of the predictive model, an experiment was conducted at the laboratory on dc motor and 3- phase induction motor to generate the required dataset to train the regression algorithms. The model evaluation metrics such MSE and the R2 value showed that the model efficiently predicted the performance of the electrical machines. en_US
dc.language.iso en en_US
dc.publisher Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa. en_US
dc.subject DC motors en_US
dc.subject Induction motor en_US
dc.subject Machine learning python en_US
dc.subject Machine learning en_US
dc.subject Performance prediction en_US
dc.title Predicting the performance of electrical machines using machine learning en_US
dc.type Conference-Full-text en_US
dc.identifier.faculty IT en_US
dc.identifier.department Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa. en_US
dc.identifier.year 2023 en_US
dc.identifier.conference 8th International Conference in Information Technology Research 2023 en_US
dc.identifier.place Moratuwa, Sri Lanka en_US
dc.identifier.pgnos pp. 1-6 en_US
dc.identifier.proceeding Proceedings of the 8th International Conference in Information Technology Research 2023 en_US
dc.identifier.email joshimanohar@presidencyuniversity.in en_US
dc.identifier.email skjha@ieee.org en_US


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  • ICITR - 2023 [47]
    International Conference on Information Technology Research (ICITR)

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