Data analytics and machine learning-based stability assessment of active grids

This 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...

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Main Author:	Sai Avinash Bavan
Other Authors:	Hung Dinh Nguyen
Format:	Final Year Project
Language:	English
Published:	Nanyang Technological University 2022
Subjects:	Engineering::Electrical and electronic engineering
Online Access:	https://hdl.handle.net/10356/157415
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Institution:	Nanyang Technological University
Language:	English

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spelling	sg-ntu-dr.10356-1574152023-07-07T19:12:50Z Data analytics and machine learning-based stability assessment of active grids Sai Avinash Bavan Hung Dinh Nguyen School of Electrical and Electronic Engineering hunghtd@ntu.edu.sg Engineering::Electrical and electronic engineering This 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 measurements Bachelor of Engineering (Electrical and Electronic Engineering) 2022-05-14T13:55:51Z 2022-05-14T13:55:51Z 2022 Final Year Project (FYP) Sai 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/10356/157415 https://hdl.handle.net/10356/157415 en application/pdf Nanyang Technological University
institution	Nanyang Technological University
building	NTU Library
continent	Asia
country	Singapore Singapore
content_provider	NTU Library
collection	DR-NTU
language	English
topic	Engineering::Electrical and electronic engineering
spellingShingle	Engineering::Electrical and electronic engineering Sai Avinash Bavan Data analytics and machine learning-based stability assessment of active grids
description	This 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 measurements
author2	Hung Dinh Nguyen
author_facet	Hung Dinh Nguyen Sai Avinash Bavan
format	Final Year Project
author	Sai Avinash Bavan
author_sort	Sai Avinash Bavan
title	Data analytics and machine learning-based stability assessment of active grids
title_short	Data analytics and machine learning-based stability assessment of active grids
title_full	Data analytics and machine learning-based stability assessment of active grids
title_fullStr	Data analytics and machine learning-based stability assessment of active grids
title_full_unstemmed	Data analytics and machine learning-based stability assessment of active grids
title_sort	data analytics and machine learning-based stability assessment of active grids
publisher	Nanyang Technological University
publishDate	2022
url	https://hdl.handle.net/10356/157415
_version_	1772827246502871040

Data analytics and machine learning-based stability assessment of active grids

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