Machine learning-based online stability assessment of power systems
The assessment of power system stability is of great significance to the research in power system operating status and power supply reliability. Under the concept of power system stability, small-signal stability, which usually occurs in the form of low-frequency oscillation, determines the power tr...
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| Format: | Thesis-Master by Coursework |
| Language: | English |
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Nanyang Technological University
2021
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| Online Access: | https://hdl.handle.net/10356/152475 |
| _version_ | 1811693149553164288 |
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| author | Zheng, Hongfei |
| author2 | Xu Yan |
| author_facet | Xu Yan Zheng, Hongfei |
| author_sort | Zheng, Hongfei |
| collection | NTU |
| description | The assessment of power system stability is of great significance to the research in power system operating status and power supply reliability. Under the concept of power system stability, small-signal stability, which usually occurs in the form of low-frequency oscillation, determines the power transmission capability in many power systems. The research goal of this dissertation is to construct a machine learning-based power system small-signal stability assessment method to evaluate the system's oscillation and small disturbance stability.
This dissertation uses three main machine learning methods, including DT (decision tree), RF (random forest), and SVM (support vector machine). By adopting the adequate technologies in feature selection, validation and testing, we succeed in building the evaluation model for power system small-signal stability. And then we select the appropriate model evaluation indicators to optimize the parameters and compare the performance in different models. A database generated from the IEEE New England 10-machine 39-bus system is used for the above processes. |
| first_indexed | 2024-10-01T06:47:05Z |
| format | Thesis-Master by Coursework |
| id | ntu-10356/152475 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:47:05Z |
| publishDate | 2021 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1524752023-07-04T17:00:16Z Machine learning-based online stability assessment of power systems Zheng, Hongfei Xu Yan School of Electrical and Electronic Engineering xuyan@ntu.edu.sg Engineering::Electrical and electronic engineering The assessment of power system stability is of great significance to the research in power system operating status and power supply reliability. Under the concept of power system stability, small-signal stability, which usually occurs in the form of low-frequency oscillation, determines the power transmission capability in many power systems. The research goal of this dissertation is to construct a machine learning-based power system small-signal stability assessment method to evaluate the system's oscillation and small disturbance stability. This dissertation uses three main machine learning methods, including DT (decision tree), RF (random forest), and SVM (support vector machine). By adopting the adequate technologies in feature selection, validation and testing, we succeed in building the evaluation model for power system small-signal stability. And then we select the appropriate model evaluation indicators to optimize the parameters and compare the performance in different models. A database generated from the IEEE New England 10-machine 39-bus system is used for the above processes. Master of Science (Power Engineering) 2021-08-19T08:30:00Z 2021-08-19T08:30:00Z 2021 Thesis-Master by Coursework Zheng, H. (2021). Machine learning-based online stability assessment of power systems. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/152475 https://hdl.handle.net/10356/152475 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering Zheng, Hongfei Machine learning-based online stability assessment of power systems |
| title | Machine learning-based online stability assessment of power systems |
| title_full | Machine learning-based online stability assessment of power systems |
| title_fullStr | Machine learning-based online stability assessment of power systems |
| title_full_unstemmed | Machine learning-based online stability assessment of power systems |
| title_short | Machine learning-based online stability assessment of power systems |
| title_sort | machine learning based online stability assessment of power systems |
| topic | Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/152475 |
| work_keys_str_mv | AT zhenghongfei machinelearningbasedonlinestabilityassessmentofpowersystems |