Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/157415
Title: Data analytics and machine learning-based stability assessment of active grids
Authors: Sai Avinash Bavan
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Publisher: Nanyang Technological University
Source: 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
Abstract: 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
URI: https://hdl.handle.net/10356/157415
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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