A Data-Driven Method for Power System Transient Instability Mode Identification Based on Knowledge Discovery and XGBoost Algorithm
Aiming at the difficulty of unstable pattern recognition after power system fault, a novel identification framework for transient instability mode identification based on knowledge discovery by accuracy maximization (KODAMA) and extreme gradient boosting (XGBoost) algorithm is proposed. In this meth...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9592755/ |
| _version_ | 1818932613652414464 |
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| author | Neng Zhang Huimin Qian Yuchao He Lirong Li Chaoyun Sun |
| author_facet | Neng Zhang Huimin Qian Yuchao He Lirong Li Chaoyun Sun |
| author_sort | Neng Zhang |
| collection | DOAJ |
| description | Aiming at the difficulty of unstable pattern recognition after power system fault, a novel identification framework for transient instability mode identification based on knowledge discovery by accuracy maximization (KODAMA) and extreme gradient boosting (XGBoost) algorithm is proposed. In this method, the transient stability of all typical fault scenarios of power system is obtained firstly by XGBoost. Then, to make full use of the structure of the raw data and mine the contained data information, a novel data mining algorithm KODAMA is introduced to cluster the mode of rotor angle in case of instability, thus to convert pattern-unlabeled case data into pattern-labeled data. Finally, based on this labeled data, to fully reflect the dynamic characteristics, a multiple XGBoost assessment strategy is designed to recognize different instable modes. The proposed technique is tested on the Nordic test system, and the results indicate that the proposed approach can provide fast and accurate recognition of instable mode and has a certain prospect of online application. |
| first_indexed | 2024-12-20T04:35:16Z |
| format | Article |
| id | doaj.art-0d7ed6350b6f4095991d74927eaf5cd8 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T04:35:16Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-0d7ed6350b6f4095991d74927eaf5cd82022-12-21T19:53:16ZengIEEEIEEE Access2169-35362021-01-01915417215418210.1109/ACCESS.2021.31240519592755A Data-Driven Method for Power System Transient Instability Mode Identification Based on Knowledge Discovery and XGBoost AlgorithmNeng Zhang0https://orcid.org/0000-0002-0514-1799Huimin Qian1Yuchao He2Lirong Li3Chaoyun Sun4Wuhan Nari Ltd., Liability Company of State Grid Power Research Institute, Wuhan, ChinaWuhan Nari Ltd., Liability Company of State Grid Power Research Institute, Wuhan, ChinaWuhan Nari Ltd., Liability Company of State Grid Power Research Institute, Wuhan, ChinaWuhan Nari Ltd., Liability Company of State Grid Power Research Institute, Wuhan, ChinaWuhan Nari Ltd., Liability Company of State Grid Power Research Institute, Wuhan, ChinaAiming at the difficulty of unstable pattern recognition after power system fault, a novel identification framework for transient instability mode identification based on knowledge discovery by accuracy maximization (KODAMA) and extreme gradient boosting (XGBoost) algorithm is proposed. In this method, the transient stability of all typical fault scenarios of power system is obtained firstly by XGBoost. Then, to make full use of the structure of the raw data and mine the contained data information, a novel data mining algorithm KODAMA is introduced to cluster the mode of rotor angle in case of instability, thus to convert pattern-unlabeled case data into pattern-labeled data. Finally, based on this labeled data, to fully reflect the dynamic characteristics, a multiple XGBoost assessment strategy is designed to recognize different instable modes. The proposed technique is tested on the Nordic test system, and the results indicate that the proposed approach can provide fast and accurate recognition of instable mode and has a certain prospect of online application.https://ieeexplore.ieee.org/document/9592755/Transient stabilityKODAMA algorithmXGBoost algorithmmachine learning |
| spellingShingle | Neng Zhang Huimin Qian Yuchao He Lirong Li Chaoyun Sun A Data-Driven Method for Power System Transient Instability Mode Identification Based on Knowledge Discovery and XGBoost Algorithm IEEE Access Transient stability KODAMA algorithm XGBoost algorithm machine learning |
| title | A Data-Driven Method for Power System Transient Instability Mode Identification Based on Knowledge Discovery and XGBoost Algorithm |
| title_full | A Data-Driven Method for Power System Transient Instability Mode Identification Based on Knowledge Discovery and XGBoost Algorithm |
| title_fullStr | A Data-Driven Method for Power System Transient Instability Mode Identification Based on Knowledge Discovery and XGBoost Algorithm |
| title_full_unstemmed | A Data-Driven Method for Power System Transient Instability Mode Identification Based on Knowledge Discovery and XGBoost Algorithm |
| title_short | A Data-Driven Method for Power System Transient Instability Mode Identification Based on Knowledge Discovery and XGBoost Algorithm |
| title_sort | data driven method for power system transient instability mode identification based on knowledge discovery and xgboost algorithm |
| topic | Transient stability KODAMA algorithm XGBoost algorithm machine learning |
| url | https://ieeexplore.ieee.org/document/9592755/ |
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