A Data-Driven Scheme Based on Sparse Projection Oblique Randomer Forests for Real-Time Dynamic Security Assessment
With the wide interconnection of power systems and extensive application of phasor measurement units (PMUs), the secure operation of power systems is facing considerable challenges. To satisfy the demand of online dynamic security assessment (DSA) for modern power systems, a data-driven scheme based...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9837900/ |
| _version_ | 1811313421682999296 |
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| author | Yanfeng Lin Xinyao Wang |
| author_facet | Yanfeng Lin Xinyao Wang |
| author_sort | Yanfeng Lin |
| collection | DOAJ |
| description | With the wide interconnection of power systems and extensive application of phasor measurement units (PMUs), the secure operation of power systems is facing considerable challenges. To satisfy the demand of online dynamic security assessment (DSA) for modern power systems, a data-driven scheme based on sparse projection oblique randomer forests (SPORF) is proposed, which includes offline training, periodic update and online assessment. In the first stage, an improved adaptive synthetic sampling (ADASYN) method is developed to mitigate the class imbalance problem for the data-driven DSA approach. Then, the SPORF-based DSA model is trained using crucial features with low redundancy selected by a feature selection procedure based on the minimal-redundancy-maximal-relevance (MRMR) criterion. In the second stage, the periodic update of the DSA model for unseen system topologies is executed to enhance the robustness of the model. In the third stage, the trained model can provide the DSA result immediately when the real-time operation information of a system is received. The satisfactory performance of the proposed scheme is demonstrated through a series of tests and the comparisons on a 23-bus system and a practical 1648-bus system. |
| first_indexed | 2024-04-13T10:54:38Z |
| format | Article |
| id | doaj.art-84daf92a8c284edcb2f632e6126e7b84 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-13T10:54:38Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-84daf92a8c284edcb2f632e6126e7b842022-12-22T02:49:34ZengIEEEIEEE Access2169-35362022-01-0110794697947910.1109/ACCESS.2022.31935069837900A Data-Driven Scheme Based on Sparse Projection Oblique Randomer Forests for Real-Time Dynamic Security AssessmentYanfeng Lin0https://orcid.org/0000-0001-6294-9631Xinyao Wang1College of International Communications, China Three Gorges University, Yichang, ChinaCollege of Electrical Engineering and New Energy, China Three Gorges University, Yichang, ChinaWith the wide interconnection of power systems and extensive application of phasor measurement units (PMUs), the secure operation of power systems is facing considerable challenges. To satisfy the demand of online dynamic security assessment (DSA) for modern power systems, a data-driven scheme based on sparse projection oblique randomer forests (SPORF) is proposed, which includes offline training, periodic update and online assessment. In the first stage, an improved adaptive synthetic sampling (ADASYN) method is developed to mitigate the class imbalance problem for the data-driven DSA approach. Then, the SPORF-based DSA model is trained using crucial features with low redundancy selected by a feature selection procedure based on the minimal-redundancy-maximal-relevance (MRMR) criterion. In the second stage, the periodic update of the DSA model for unseen system topologies is executed to enhance the robustness of the model. In the third stage, the trained model can provide the DSA result immediately when the real-time operation information of a system is received. The satisfactory performance of the proposed scheme is demonstrated through a series of tests and the comparisons on a 23-bus system and a practical 1648-bus system.https://ieeexplore.ieee.org/document/9837900/Dynamic security assessmentdata-drivendata oversamplingfeature selectionsparse projection oblique randomer forests |
| spellingShingle | Yanfeng Lin Xinyao Wang A Data-Driven Scheme Based on Sparse Projection Oblique Randomer Forests for Real-Time Dynamic Security Assessment IEEE Access Dynamic security assessment data-driven data oversampling feature selection sparse projection oblique randomer forests |
| title | A Data-Driven Scheme Based on Sparse Projection Oblique Randomer Forests for Real-Time Dynamic Security Assessment |
| title_full | A Data-Driven Scheme Based on Sparse Projection Oblique Randomer Forests for Real-Time Dynamic Security Assessment |
| title_fullStr | A Data-Driven Scheme Based on Sparse Projection Oblique Randomer Forests for Real-Time Dynamic Security Assessment |
| title_full_unstemmed | A Data-Driven Scheme Based on Sparse Projection Oblique Randomer Forests for Real-Time Dynamic Security Assessment |
| title_short | A Data-Driven Scheme Based on Sparse Projection Oblique Randomer Forests for Real-Time Dynamic Security Assessment |
| title_sort | data driven scheme based on sparse projection oblique randomer forests for real time dynamic security assessment |
| topic | Dynamic security assessment data-driven data oversampling feature selection sparse projection oblique randomer forests |
| url | https://ieeexplore.ieee.org/document/9837900/ |
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