Machine learning and control applications for intelligent grids

In this project, we investigate the capabilities of Gaussian Process Regression (GPR) in predicting power flow parameters in a power grid. Data points are produced with different types of probability distribution, uniform distribution, and Gaussian distribution, for the GPR model to perform on. Addi...

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Main Author: Fong, Ye Cheng
Other Authors: Hung Dinh Nguyen
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2024
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Online Access:https://hdl.handle.net/10356/176492
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Institution: Nanyang Technological University
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spelling sg-ntu-dr.10356-1764922024-05-17T15:45:41Z Machine learning and control applications for intelligent grids Fong, Ye Cheng Hung Dinh Nguyen School of Electrical and Electronic Engineering hunghtd@ntu.edu.sg Computer and Information Science Engineering In this project, we investigate the capabilities of Gaussian Process Regression (GPR) in predicting power flow parameters in a power grid. Data points are produced with different types of probability distribution, uniform distribution, and Gaussian distribution, for the GPR model to perform on. Additionally, different types of kernels, the Squared Exponential, Matern, and Rational Quadratic kernels, were used to sieve out the most suited kernel type for our GPR model for this experiment. Combining kernels are also trialed to see if their performance edges out on the standard kernels. This project attempts to explore the versatility of GPR because of its unique feature of predicting uncertainty alongside predicted values. We aim to see if our GPR model is able to perform well under different conditions, for example, comparing the performance of predicting a uniform distributed dataset with the Matern kernel and the performance of a GPR model using the Rational Quadratic kernel. Tests were done using Matlab and Python, with readily available packages like MATPOWER and GPML for us to generate data and excute GPR properties respectively. The 33-bus system from Baran & Wu is used to produce power flow results data, then GPR training and testing is done in Matlab and Python. Results display that the type of probability distribution plays a part in GPR performance as the GPR predictions on a Gaussian distributed dataset produced better accuracy results than the predictions on a uniform distributed dataset. The type of kernel used did not play a big part in the accuracy results. Bachelor's degree 2024-05-17T02:33:53Z 2024-05-17T02:33:53Z 2024 Final Year Project (FYP) Fong, Y. C. (2024). Machine learning and control applications for intelligent grids. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/176492 https://hdl.handle.net/10356/176492 en A1062-231 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 Computer and Information Science
Engineering
spellingShingle Computer and Information Science
Engineering
Fong, Ye Cheng
Machine learning and control applications for intelligent grids
description In this project, we investigate the capabilities of Gaussian Process Regression (GPR) in predicting power flow parameters in a power grid. Data points are produced with different types of probability distribution, uniform distribution, and Gaussian distribution, for the GPR model to perform on. Additionally, different types of kernels, the Squared Exponential, Matern, and Rational Quadratic kernels, were used to sieve out the most suited kernel type for our GPR model for this experiment. Combining kernels are also trialed to see if their performance edges out on the standard kernels. This project attempts to explore the versatility of GPR because of its unique feature of predicting uncertainty alongside predicted values. We aim to see if our GPR model is able to perform well under different conditions, for example, comparing the performance of predicting a uniform distributed dataset with the Matern kernel and the performance of a GPR model using the Rational Quadratic kernel. Tests were done using Matlab and Python, with readily available packages like MATPOWER and GPML for us to generate data and excute GPR properties respectively. The 33-bus system from Baran & Wu is used to produce power flow results data, then GPR training and testing is done in Matlab and Python. Results display that the type of probability distribution plays a part in GPR performance as the GPR predictions on a Gaussian distributed dataset produced better accuracy results than the predictions on a uniform distributed dataset. The type of kernel used did not play a big part in the accuracy results.
author2 Hung Dinh Nguyen
author_facet Hung Dinh Nguyen
Fong, Ye Cheng
format Final Year Project
author Fong, Ye Cheng
author_sort Fong, Ye Cheng
title Machine learning and control applications for intelligent grids
title_short Machine learning and control applications for intelligent grids
title_full Machine learning and control applications for intelligent grids
title_fullStr Machine learning and control applications for intelligent grids
title_full_unstemmed Machine learning and control applications for intelligent grids
title_sort machine learning and control applications for intelligent grids
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/176492
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