Data-analytics for power system stability assessment
With the increasing integration of phasor measurement units (PMUs) and supervisory control and data acquisition (SCADA) in power systems, intelligent data-analytics for short-term voltage stability assessment becomes achievable. This task requires fast response and accurate conclusion, especially, t...
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| Format: | Thesis-Master by Coursework |
| Language: | English |
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Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/158942 |
| _version_ | 1826118662732382208 |
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| author | Tang, Yuchi |
| author2 | Xu Yan |
| author_facet | Xu Yan Tang, Yuchi |
| author_sort | Tang, Yuchi |
| collection | NTU |
| description | With the increasing integration of phasor measurement units (PMUs) and supervisory control and data acquisition (SCADA) in power systems, intelligent data-analytics for short-term voltage stability assessment becomes achievable. This task requires fast response and accurate conclusion, especially, to avoid wrong conclusions for the actual unstable cases. Given this, an intelligent post-fault short-term voltage stability (STVS) assessment method is proposed in this research. By introducing Gramian Angular Field (GAF) transform, two-dimensional convolutional neural network (2D-CNN), and adaptive confidence interval (ACI), the proposed method shows better performance to carry out the task. The related tests are based on the New England 10-machine 39-bus system with an obtained 6536-case dataset. |
| first_indexed | 2024-10-01T04:47:08Z |
| format | Thesis-Master by Coursework |
| id | ntu-10356/158942 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:47:08Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1589422023-07-04T17:52:53Z Data-analytics for power system stability assessment Tang, Yuchi Xu Yan School of Electrical and Electronic Engineering xuyan@ntu.edu.sg Engineering::Electrical and electronic engineering With the increasing integration of phasor measurement units (PMUs) and supervisory control and data acquisition (SCADA) in power systems, intelligent data-analytics for short-term voltage stability assessment becomes achievable. This task requires fast response and accurate conclusion, especially, to avoid wrong conclusions for the actual unstable cases. Given this, an intelligent post-fault short-term voltage stability (STVS) assessment method is proposed in this research. By introducing Gramian Angular Field (GAF) transform, two-dimensional convolutional neural network (2D-CNN), and adaptive confidence interval (ACI), the proposed method shows better performance to carry out the task. The related tests are based on the New England 10-machine 39-bus system with an obtained 6536-case dataset. Master of Science (Computer Control and Automation) 2022-06-02T12:09:35Z 2022-06-02T12:09:35Z 2022 Thesis-Master by Coursework Tang, Y. (2022). Data-analytics for power system stability assessment. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158942 https://hdl.handle.net/10356/158942 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering Tang, Yuchi Data-analytics for power system stability assessment |
| title | Data-analytics for power system stability assessment |
| title_full | Data-analytics for power system stability assessment |
| title_fullStr | Data-analytics for power system stability assessment |
| title_full_unstemmed | Data-analytics for power system stability assessment |
| title_short | Data-analytics for power system stability assessment |
| title_sort | data analytics for power system stability assessment |
| topic | Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/158942 |
| work_keys_str_mv | AT tangyuchi dataanalyticsforpowersystemstabilityassessment |