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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書目詳細資料
主要作者: Sai Avinash Bavan
其他作者: Hung Dinh Nguyen
格式: Final Year Project
語言:English
出版: Nanyang Technological University 2022
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在線閱讀:https://hdl.handle.net/10356/157415
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機構: Nanyang Technological University
語言: English
實物特徵
總結: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