Power Quality Management and Classification for Smart Grid Application using Machine Learning
The Efficient Wavelet-based Convolutional Transformer network (EWT-ConvT) is proposed to detect power quality disturbances in time-frequency domain using attention mechanism. The support of machine learning further improves the network accuracy with synthetic signal generation and less system comple...
| Main Author: | |
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| Format: | Thesis |
| Published: |
Curtin University
2023
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| Online Access: | http://hdl.handle.net/20.500.11937/92726 |
| _version_ | 1848765657327337472 |
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| author | Chiam, Dar Hung |
| author_facet | Chiam, Dar Hung |
| author_sort | Chiam, Dar Hung |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The Efficient Wavelet-based Convolutional Transformer network (EWT-ConvT) is proposed to detect power quality disturbances in time-frequency domain using attention mechanism. The support of machine learning further improves the network accuracy with synthetic signal generation and less system complexity under practical environment. The proposed EWT-ConvT can achieve 94.42% accuracy which is superior than other deep learning models. The detection of disturbances using EWT-ConvT can also be implemented into smart grid applications for real-time embedded system development. |
| first_indexed | 2025-11-14T11:38:44Z |
| format | Thesis |
| id | curtin-20.500.11937-92726 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:38:44Z |
| publishDate | 2023 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-927262023-07-10T03:29:30Z Power Quality Management and Classification for Smart Grid Application using Machine Learning Chiam, Dar Hung The Efficient Wavelet-based Convolutional Transformer network (EWT-ConvT) is proposed to detect power quality disturbances in time-frequency domain using attention mechanism. The support of machine learning further improves the network accuracy with synthetic signal generation and less system complexity under practical environment. The proposed EWT-ConvT can achieve 94.42% accuracy which is superior than other deep learning models. The detection of disturbances using EWT-ConvT can also be implemented into smart grid applications for real-time embedded system development. 2023 Thesis http://hdl.handle.net/20.500.11937/92726 Curtin University fulltext |
| spellingShingle | Chiam, Dar Hung Power Quality Management and Classification for Smart Grid Application using Machine Learning |
| title | Power Quality Management and Classification for Smart
Grid Application using Machine Learning |
| title_full | Power Quality Management and Classification for Smart
Grid Application using Machine Learning |
| title_fullStr | Power Quality Management and Classification for Smart
Grid Application using Machine Learning |
| title_full_unstemmed | Power Quality Management and Classification for Smart
Grid Application using Machine Learning |
| title_short | Power Quality Management and Classification for Smart
Grid Application using Machine Learning |
| title_sort | power quality management and classification for smart
grid application using machine learning |
| url | http://hdl.handle.net/20.500.11937/92726 |