Power system transient security assessment based on multi-channel time series data mining
In the context of the clean energy revolution and the high penetration of renewables and power electronics, data-driven Transient Security Assessment (TSA) models can significantly reduce the computational burden of power system TSA and adapt to the quickly changing operating states of modern power...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-11-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484722015955 |
| _version_ | 1797900660295860224 |
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| author | Kangkang Wang Han Diao Wei Wei Tannan Xiao Ying Chen Bo Zhou |
| author_facet | Kangkang Wang Han Diao Wei Wei Tannan Xiao Ying Chen Bo Zhou |
| author_sort | Kangkang Wang |
| collection | DOAJ |
| description | In the context of the clean energy revolution and the high penetration of renewables and power electronics, data-driven Transient Security Assessment (TSA) models can significantly reduce the computational burden of power system TSA and adapt to the quickly changing operating states of modern power systems. In this paper, a multi-channel time series data mining framework is proposed to enhance the performance of data-driven TSA models. During the training procedures, a Lagrangian dual framework is adopted to enhance the feature extraction ability of different types of disturbed system trajectories. The proposed method is adopted to an ordinary Long Short-Term Memory (LSTM) model and numerical tests are carried out in the IEEE-39 system. The test results show that the proposed method can effectively improve the performance and generalization ability of the data-driven TSA model. |
| first_indexed | 2024-04-10T08:49:30Z |
| format | Article |
| id | doaj.art-cd94d36bd53f4f828009737a3453f689 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-04-10T08:49:30Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-cd94d36bd53f4f828009737a3453f6892023-02-22T04:31:33ZengElsevierEnergy Reports2352-48472022-11-018843851Power system transient security assessment based on multi-channel time series data miningKangkang Wang0Han Diao1Wei Wei2Tannan Xiao3Ying Chen4Bo Zhou5Electric Power Research Institute, State Grid Sichuan Electric Power Company, Chengdu 610095, ChinaTsinghua University, Beijing 10084, ChinaElectric Power Research Institute, State Grid Sichuan Electric Power Company, Chengdu 610095, ChinaTsinghua University, Beijing 10084, ChinaTsinghua University, Beijing 10084, China; Corresponding author.Electric Power Research Institute, State Grid Sichuan Electric Power Company, Chengdu 610095, ChinaIn the context of the clean energy revolution and the high penetration of renewables and power electronics, data-driven Transient Security Assessment (TSA) models can significantly reduce the computational burden of power system TSA and adapt to the quickly changing operating states of modern power systems. In this paper, a multi-channel time series data mining framework is proposed to enhance the performance of data-driven TSA models. During the training procedures, a Lagrangian dual framework is adopted to enhance the feature extraction ability of different types of disturbed system trajectories. The proposed method is adopted to an ordinary Long Short-Term Memory (LSTM) model and numerical tests are carried out in the IEEE-39 system. The test results show that the proposed method can effectively improve the performance and generalization ability of the data-driven TSA model.http://www.sciencedirect.com/science/article/pii/S2352484722015955Power systemTransient stabilityTime seriesDeep learningMulti-channel |
| spellingShingle | Kangkang Wang Han Diao Wei Wei Tannan Xiao Ying Chen Bo Zhou Power system transient security assessment based on multi-channel time series data mining Energy Reports Power system Transient stability Time series Deep learning Multi-channel |
| title | Power system transient security assessment based on multi-channel time series data mining |
| title_full | Power system transient security assessment based on multi-channel time series data mining |
| title_fullStr | Power system transient security assessment based on multi-channel time series data mining |
| title_full_unstemmed | Power system transient security assessment based on multi-channel time series data mining |
| title_short | Power system transient security assessment based on multi-channel time series data mining |
| title_sort | power system transient security assessment based on multi channel time series data mining |
| topic | Power system Transient stability Time series Deep learning Multi-channel |
| url | http://www.sciencedirect.com/science/article/pii/S2352484722015955 |
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