A model-free method to detect the risk and locate the sources of sub-synchronous oscillations in a large-scale renewable power system
Sub-synchronous oscillations (SSOs) are serious threat to the stability of power systems integrated with renewable generation, and locating SSO sources is important to counteract SSOs. However, model-based methods are inefficient in this task when the parameters of renewable generation are unknown....
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| Format: | Article |
| Language: | English |
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Elsevier
2025-04-01
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| Series: | International Journal of Electrical Power & Energy Systems |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0142061525000110 |
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| author | Yufan He Wenjuan Du Qiang Fu H.F. Wang |
| author_facet | Yufan He Wenjuan Du Qiang Fu H.F. Wang |
| author_sort | Yufan He |
| collection | DOAJ |
| description | Sub-synchronous oscillations (SSOs) are serious threat to the stability of power systems integrated with renewable generation, and locating SSO sources is important to counteract SSOs. However, model-based methods are inefficient in this task when the parameters of renewable generation are unknown. Measurement-driven machine learning-based approaches have emerged as alternatives, but face the challenge of unbalanced training data in power systems, where non-oscillation datasets outnumber oscillation datasets. Although the transfer learning (TL) algorithm helps mitigate the issue, it requires certain power system parameters to reconstruct the source domain. Subsequently, incorrect parameters limit the applicability of TL. To solve the problem, this study proposes a novel model-free method to detect SSO risk and locate the source. The proposed method applies the deep learning support vector data description and label spreading approaches. The training process of the proposed method only requires datasets measured from the power system with a few SSO source labels. Thus, the method is truly model-free and requires no parameters of the power system. Case studies are presented in this paper to demonstrate and validate the performance of the proposed method. |
| format | Article |
| id | doaj-art-7f6ffc661b2744b7976efed0e5dd44c2 |
| institution | DOAJ |
| issn | 0142-0615 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Electrical Power & Energy Systems |
| spelling | doaj-art-7f6ffc661b2744b7976efed0e5dd44c22025-08-20T03:11:43ZengElsevierInternational Journal of Electrical Power & Energy Systems0142-06152025-04-0116511046010.1016/j.ijepes.2025.110460A model-free method to detect the risk and locate the sources of sub-synchronous oscillations in a large-scale renewable power systemYufan He0Wenjuan Du1Qiang Fu2H.F. Wang3College of Electrical Engineering, Sichuan University, Chengdu 610065 PR ChinaCorresponding author.; College of Electrical Engineering, Sichuan University, Chengdu 610065 PR ChinaCollege of Electrical Engineering, Sichuan University, Chengdu 610065 PR ChinaCollege of Electrical Engineering, Sichuan University, Chengdu 610065 PR ChinaSub-synchronous oscillations (SSOs) are serious threat to the stability of power systems integrated with renewable generation, and locating SSO sources is important to counteract SSOs. However, model-based methods are inefficient in this task when the parameters of renewable generation are unknown. Measurement-driven machine learning-based approaches have emerged as alternatives, but face the challenge of unbalanced training data in power systems, where non-oscillation datasets outnumber oscillation datasets. Although the transfer learning (TL) algorithm helps mitigate the issue, it requires certain power system parameters to reconstruct the source domain. Subsequently, incorrect parameters limit the applicability of TL. To solve the problem, this study proposes a novel model-free method to detect SSO risk and locate the source. The proposed method applies the deep learning support vector data description and label spreading approaches. The training process of the proposed method only requires datasets measured from the power system with a few SSO source labels. Thus, the method is truly model-free and requires no parameters of the power system. Case studies are presented in this paper to demonstrate and validate the performance of the proposed method.http://www.sciencedirect.com/science/article/pii/S0142061525000110Power system stabilitySub-synchronous OscillationsMachine learningOscillation source tracing |
| spellingShingle | Yufan He Wenjuan Du Qiang Fu H.F. Wang A model-free method to detect the risk and locate the sources of sub-synchronous oscillations in a large-scale renewable power system International Journal of Electrical Power & Energy Systems Power system stability Sub-synchronous Oscillations Machine learning Oscillation source tracing |
| title | A model-free method to detect the risk and locate the sources of sub-synchronous oscillations in a large-scale renewable power system |
| title_full | A model-free method to detect the risk and locate the sources of sub-synchronous oscillations in a large-scale renewable power system |
| title_fullStr | A model-free method to detect the risk and locate the sources of sub-synchronous oscillations in a large-scale renewable power system |
| title_full_unstemmed | A model-free method to detect the risk and locate the sources of sub-synchronous oscillations in a large-scale renewable power system |
| title_short | A model-free method to detect the risk and locate the sources of sub-synchronous oscillations in a large-scale renewable power system |
| title_sort | model free method to detect the risk and locate the sources of sub synchronous oscillations in a large scale renewable power system |
| topic | Power system stability Sub-synchronous Oscillations Machine learning Oscillation source tracing |
| url | http://www.sciencedirect.com/science/article/pii/S0142061525000110 |
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