AI-based power system stability assessment and GUI design

This article introduces some methods of machine learning (ML) and artificial intelligence (AI) used to assess the stability of the power system. Four models are mainly used, including Support Vector Machine (SVM), Multilayer Perceptron (MLP), Convolutional Neural Network (CNN) and Long Short-term Me...

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Main Author: Peng, Xuerui
Other Authors: Xu Yan
Format: Thesis-Master by Coursework
Language:English
Published: Nanyang Technological University 2023
Subjects:
Online Access:https://hdl.handle.net/10356/170219
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1702192023-09-08T15:42:29Z AI-based power system stability assessment and GUI design Peng, Xuerui Xu Yan School of Electrical and Electronic Engineering xuyan@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Engineering::Electrical and electronic engineering::Electric power This article introduces some methods of machine learning (ML) and artificial intelligence (AI) used to assess the stability of the power system. Four models are mainly used, including Support Vector Machine (SVM), Multilayer Perceptron (MLP), Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM). The four methods’ theoretical motivation is conceptually explained, and they are tested with the 7127 feature and label data. The results along with the accuracy, the recall rate, the mean absolute error as well as the mean squared error are being analyzed. During pre-processing procedure, Principal Components Analysis (PCA) is used to reduce dimension and select features. With the result of the AI model, a GUI is designed to display the label result directly. Master of Science (Computer Control and Automation) 2023-09-04T01:52:35Z 2023-09-04T01:52:35Z 2023 Thesis-Master by Coursework Peng, X. (2023). AI-based power system stability assessment and GUI design. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/170219 https://hdl.handle.net/10356/170219 en D-257-22231-05932 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::Computer science and engineering::Computing methodologies::Artificial intelligence
Engineering::Electrical and electronic engineering::Electric power
spellingShingle Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Engineering::Electrical and electronic engineering::Electric power
Peng, Xuerui
AI-based power system stability assessment and GUI design
description This article introduces some methods of machine learning (ML) and artificial intelligence (AI) used to assess the stability of the power system. Four models are mainly used, including Support Vector Machine (SVM), Multilayer Perceptron (MLP), Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM). The four methods’ theoretical motivation is conceptually explained, and they are tested with the 7127 feature and label data. The results along with the accuracy, the recall rate, the mean absolute error as well as the mean squared error are being analyzed. During pre-processing procedure, Principal Components Analysis (PCA) is used to reduce dimension and select features. With the result of the AI model, a GUI is designed to display the label result directly.
author2 Xu Yan
author_facet Xu Yan
Peng, Xuerui
format Thesis-Master by Coursework
author Peng, Xuerui
author_sort Peng, Xuerui
title AI-based power system stability assessment and GUI design
title_short AI-based power system stability assessment and GUI design
title_full AI-based power system stability assessment and GUI design
title_fullStr AI-based power system stability assessment and GUI design
title_full_unstemmed AI-based power system stability assessment and GUI design
title_sort ai-based power system stability assessment and gui design
publisher Nanyang Technological University
publishDate 2023
url https://hdl.handle.net/10356/170219
_version_ 1779156356705550336