Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/157415
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dc.contributor.authorSai Avinash Bavanen_US
dc.date.accessioned2022-05-14T13:55:51Z-
dc.date.available2022-05-14T13:55:51Z-
dc.date.issued2022-
dc.identifier.citationSai Avinash Bavan (2022). Data analytics and machine learning-based stability assessment of active grids. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157415en_US
dc.identifier.urihttps://hdl.handle.net/10356/157415-
dc.description.abstractThis project focuses on using Gaussian Process (GP) as a machine learning tool to solve Probabilistic Optimal Power Flow for systems with load uncertainties and renewable sources. It also tests the accuracy and competency of GP-POPF, by the use of different kernels, under the different number of bus systems. With results obtained with the use of GP for POPF, they were compared to results obtained from the traditional use of Monte-Carlo Simulations (MCS) with the purpose of minimizing error measurementsen_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleData analytics and machine learning-based stability assessment of active gridsen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorHung Dinh Nguyenen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineering (Electrical and Electronic Engineering)en_US
dc.contributor.supervisoremailhunghtd@ntu.edu.sgen_US
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Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
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