Hybrid analytical and data-driven model based instance-transfer method for power system online transient stability assessment
Data-driven methods are widely recognized and generate conducive results for online transient stability assessment. However, the tedious and time-consuming process of sample collection is often overlooked. The functioning of power systems involves repetitive sample collection due to the constant var...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/181642 |
| _version_ | 1826120992931446784 |
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| author | Li, Feng Wang, Qi Tang, Yi Xu, Yan Dang, Jie |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Li, Feng Wang, Qi Tang, Yi Xu, Yan Dang, Jie |
| author_sort | Li, Feng |
| collection | NTU |
| description | Data-driven methods are widely recognized and generate conducive results for online transient stability assessment. However, the tedious and time-consuming process of sample collection is often overlooked. The functioning of power systems involves repetitive sample collection due to the constant variations occurring in the operation mode, thereby highlighting the importance of collection efficiency. As a means to achieve high sample collection efficiency following the operation mode change, we propose a novel instance-transfer method based on compression and matching strategy, which facilitates the direct acquisition of useful previous samples, used for creating the new sample base. Additionally, we present a hybrid model to ensure rationality in the process of sample similarity comparison and selection, where features of analytical modeling with special significance are introduced into data-driven methods. At the same time, a data-driven method can also be integrated in the hybrid model to achieve rapid error correction of analytical models, enabling fast and accurate post-disturbance transient stability assessment. As a paradigm, we consider a scheme for online critical clearing time estimation, where integrated extended equal area criterion and extreme learning machine are employed as analytical model part and data-driven error correction model part, respectively. Derived results validate the credible efficacy of the proposed method. |
| first_indexed | 2025-03-09T12:51:29Z |
| format | Journal Article |
| id | ntu-10356/181642 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-03-09T12:51:29Z |
| publishDate | 2024 |
| record_format | dspace |
| spelling | ntu-10356/1816422024-12-13T15:42:21Z Hybrid analytical and data-driven model based instance-transfer method for power system online transient stability assessment Li, Feng Wang, Qi Tang, Yi Xu, Yan Dang, Jie School of Electrical and Electronic Engineering Center for Power Engineering Engineering Critical clearing time Extreme learning machine Data-driven methods are widely recognized and generate conducive results for online transient stability assessment. However, the tedious and time-consuming process of sample collection is often overlooked. The functioning of power systems involves repetitive sample collection due to the constant variations occurring in the operation mode, thereby highlighting the importance of collection efficiency. As a means to achieve high sample collection efficiency following the operation mode change, we propose a novel instance-transfer method based on compression and matching strategy, which facilitates the direct acquisition of useful previous samples, used for creating the new sample base. Additionally, we present a hybrid model to ensure rationality in the process of sample similarity comparison and selection, where features of analytical modeling with special significance are introduced into data-driven methods. At the same time, a data-driven method can also be integrated in the hybrid model to achieve rapid error correction of analytical models, enabling fast and accurate post-disturbance transient stability assessment. As a paradigm, we consider a scheme for online critical clearing time estimation, where integrated extended equal area criterion and extreme learning machine are employed as analytical model part and data-driven error correction model part, respectively. Derived results validate the credible efficacy of the proposed method. Published version This work was supported by Central China Branch of State Grid Corporation of China (Characteristics Analysis and Operation Control Technology Research on Power Grid Adapting to Large-scale and Strong Sparse New Energy). 2024-12-11T06:25:30Z 2024-12-11T06:25:30Z 2024 Journal Article Li, F., Wang, Q., Tang, Y., Xu, Y. & Dang, J. (2024). Hybrid analytical and data-driven model based instance-transfer method for power system online transient stability assessment. CSEE Journal of Power and Energy Systems, 10(4), 1664-1675. https://dx.doi.org/10.17775/CSEEJPES.2020.03880 2096-0042 https://hdl.handle.net/10356/181642 10.17775/CSEEJPES.2020.03880 2-s2.0-85204049216 4 10 1664 1675 en CSEE Journal of Power and Energy Systems © 2020 CSEE. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). application/pdf |
| spellingShingle | Engineering Critical clearing time Extreme learning machine Li, Feng Wang, Qi Tang, Yi Xu, Yan Dang, Jie Hybrid analytical and data-driven model based instance-transfer method for power system online transient stability assessment |
| title | Hybrid analytical and data-driven model based instance-transfer method for power system online transient stability assessment |
| title_full | Hybrid analytical and data-driven model based instance-transfer method for power system online transient stability assessment |
| title_fullStr | Hybrid analytical and data-driven model based instance-transfer method for power system online transient stability assessment |
| title_full_unstemmed | Hybrid analytical and data-driven model based instance-transfer method for power system online transient stability assessment |
| title_short | Hybrid analytical and data-driven model based instance-transfer method for power system online transient stability assessment |
| title_sort | hybrid analytical and data driven model based instance transfer method for power system online transient stability assessment |
| topic | Engineering Critical clearing time Extreme learning machine |
| url | https://hdl.handle.net/10356/181642 |
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