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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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 |
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Computer and Information Science Engineering Fong, Ye Cheng Machine learning and control applications for intelligent grids |
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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. |
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Hung Dinh Nguyen |
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Hung Dinh Nguyen Fong, Ye Cheng |
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Final Year Project |
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Fong, Ye Cheng |
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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 |
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Nanyang Technological University |
publishDate |
2024 |
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https://hdl.handle.net/10356/176492 |
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