Extracting inter-area oscillation modes using local measurements and data-driven stochastic subspace technique
Abstract In this paper, a data-driven stochastic subspace identification (SSI-DATA) technique is proposed as an advanced stochastic system identification (SSI) to extract the inter-area oscillation modes of a power system from wide-area measurements. For accurate and robust extraction of the modes’...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2017-03-01
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| Series: | Journal of Modern Power Systems and Clean Energy |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1007/s40565-017-0271-6 |
| _version_ | 1819091692855230464 |
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| author | Deyou YANG Guowei CAI Kevin CHAN |
| author_facet | Deyou YANG Guowei CAI Kevin CHAN |
| author_sort | Deyou YANG |
| collection | DOAJ |
| description | Abstract In this paper, a data-driven stochastic subspace identification (SSI-DATA) technique is proposed as an advanced stochastic system identification (SSI) to extract the inter-area oscillation modes of a power system from wide-area measurements. For accurate and robust extraction of the modes’ parameters (frequency, damping and mode shape), SSI has already been verified as an effective identification algorithm for output-only modal analysis. The new feature of the proposed SSI-DATA applied to inter-area oscillation modal identification lies in its ability to select the eigenvalue automatically. The effectiveness of the proposed scheme has been fully studied and verified, first using transient stability data generated from the IEEE 16-generator 5-area test system, and then using recorded data from an actual event using a Chinese wide-area measurement system (WAMS) in 2004. The results from the simulated and recorded measurements have validated the reliability and applicability of the SSI-DATA technique in power system low frequency oscillation analysis. |
| first_indexed | 2024-12-21T22:43:46Z |
| format | Article |
| id | doaj.art-f3c69ecc96c048bcbab4e6217547cddc |
| institution | Directory Open Access Journal |
| issn | 2196-5625 2196-5420 |
| language | English |
| last_indexed | 2024-12-21T22:43:46Z |
| publishDate | 2017-03-01 |
| publisher | IEEE |
| record_format | Article |
| series | Journal of Modern Power Systems and Clean Energy |
| spelling | doaj.art-f3c69ecc96c048bcbab4e6217547cddc2022-12-21T18:47:46ZengIEEEJournal of Modern Power Systems and Clean Energy2196-56252196-54202017-03-015570471210.1007/s40565-017-0271-6Extracting inter-area oscillation modes using local measurements and data-driven stochastic subspace techniqueDeyou YANG0Guowei CAI1Kevin CHAN2School of Electrical Engineering, Northeast Dianli UniversitySchool of Electrical Engineering, Northeast Dianli UniversityDepartment of Electrical Engineering, The Hong Kong Polytechnic UniversityAbstract In this paper, a data-driven stochastic subspace identification (SSI-DATA) technique is proposed as an advanced stochastic system identification (SSI) to extract the inter-area oscillation modes of a power system from wide-area measurements. For accurate and robust extraction of the modes’ parameters (frequency, damping and mode shape), SSI has already been verified as an effective identification algorithm for output-only modal analysis. The new feature of the proposed SSI-DATA applied to inter-area oscillation modal identification lies in its ability to select the eigenvalue automatically. The effectiveness of the proposed scheme has been fully studied and verified, first using transient stability data generated from the IEEE 16-generator 5-area test system, and then using recorded data from an actual event using a Chinese wide-area measurement system (WAMS) in 2004. The results from the simulated and recorded measurements have validated the reliability and applicability of the SSI-DATA technique in power system low frequency oscillation analysis.http://link.springer.com/article/10.1007/s40565-017-0271-6Data-driven stochastic subspace identification (SSI-DATA)Power system inter-area oscillationWide-area measurement systems (WAMS)Modal analysis |
| spellingShingle | Deyou YANG Guowei CAI Kevin CHAN Extracting inter-area oscillation modes using local measurements and data-driven stochastic subspace technique Journal of Modern Power Systems and Clean Energy Data-driven stochastic subspace identification (SSI-DATA) Power system inter-area oscillation Wide-area measurement systems (WAMS) Modal analysis |
| title | Extracting inter-area oscillation modes using local measurements and data-driven stochastic subspace technique |
| title_full | Extracting inter-area oscillation modes using local measurements and data-driven stochastic subspace technique |
| title_fullStr | Extracting inter-area oscillation modes using local measurements and data-driven stochastic subspace technique |
| title_full_unstemmed | Extracting inter-area oscillation modes using local measurements and data-driven stochastic subspace technique |
| title_short | Extracting inter-area oscillation modes using local measurements and data-driven stochastic subspace technique |
| title_sort | extracting inter area oscillation modes using local measurements and data driven stochastic subspace technique |
| topic | Data-driven stochastic subspace identification (SSI-DATA) Power system inter-area oscillation Wide-area measurement systems (WAMS) Modal analysis |
| url | http://link.springer.com/article/10.1007/s40565-017-0271-6 |
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