Predictive Online Transient Stability Assessment for Enhancing Efficiency
Online transient stability assessment (TSA) is essential for the reliable operation of power systems. The increasing deployment of phasor measurement units (PMUs) across power systems provides a wealth of fast, accurate, and detailed transient data, offering significant opportunities to enhance onli...
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IEEE
2024-01-01
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| Series: | IEEE Open Access Journal of Power and Energy |
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| Online Access: | https://ieeexplore.ieee.org/document/10510341/ |
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| author | Rui Ma Sara Eftekharnejad Chen Zhong |
| author_facet | Rui Ma Sara Eftekharnejad Chen Zhong |
| author_sort | Rui Ma |
| collection | DOAJ |
| description | Online transient stability assessment (TSA) is essential for the reliable operation of power systems. The increasing deployment of phasor measurement units (PMUs) across power systems provides a wealth of fast, accurate, and detailed transient data, offering significant opportunities to enhance online TSA. Unlike conventional data-driven methods that require large volumes of transient PMU data for accurate TSA, this paper develops a new TSA method that requires significantly less data. This data reduction is enabled by generative and adversarial networks (GAN), which predict voltage time-series data following a transient event, thereby minimizing the need for extensive data. A classifier embedded in the generative network deploys the predicted data to determine the stability of the system. The developed method preserves the temporal correlations in the multivariate time series data. Hence, compared to the state-of-the-art methods, it is more accurate using only one sample of the measured PMU data and has a shorter response time. |
| format | Article |
| id | doaj-art-256b7bed60694110870a0aa7ae388434 |
| institution | Kabale University |
| issn | 2687-7910 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Access Journal of Power and Energy |
| spelling | doaj-art-256b7bed60694110870a0aa7ae3884342025-01-21T00:03:03ZengIEEEIEEE Open Access Journal of Power and Energy2687-79102024-01-011120721710.1109/OAJPE.2024.339517710510341Predictive Online Transient Stability Assessment for Enhancing EfficiencyRui Ma0https://orcid.org/0000-0002-4962-4776Sara Eftekharnejad1https://orcid.org/0000-0003-4313-6840Chen Zhong2https://orcid.org/0000-0003-3934-4436Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY, USADepartment of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY, USADepartment of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY, USAOnline transient stability assessment (TSA) is essential for the reliable operation of power systems. The increasing deployment of phasor measurement units (PMUs) across power systems provides a wealth of fast, accurate, and detailed transient data, offering significant opportunities to enhance online TSA. Unlike conventional data-driven methods that require large volumes of transient PMU data for accurate TSA, this paper develops a new TSA method that requires significantly less data. This data reduction is enabled by generative and adversarial networks (GAN), which predict voltage time-series data following a transient event, thereby minimizing the need for extensive data. A classifier embedded in the generative network deploys the predicted data to determine the stability of the system. The developed method preserves the temporal correlations in the multivariate time series data. Hence, compared to the state-of-the-art methods, it is more accurate using only one sample of the measured PMU data and has a shorter response time.https://ieeexplore.ieee.org/document/10510341/Classificationgenerative adversarial networksphasor measurement unittransient stability |
| spellingShingle | Rui Ma Sara Eftekharnejad Chen Zhong Predictive Online Transient Stability Assessment for Enhancing Efficiency IEEE Open Access Journal of Power and Energy Classification generative adversarial networks phasor measurement unit transient stability |
| title | Predictive Online Transient Stability Assessment for Enhancing Efficiency |
| title_full | Predictive Online Transient Stability Assessment for Enhancing Efficiency |
| title_fullStr | Predictive Online Transient Stability Assessment for Enhancing Efficiency |
| title_full_unstemmed | Predictive Online Transient Stability Assessment for Enhancing Efficiency |
| title_short | Predictive Online Transient Stability Assessment for Enhancing Efficiency |
| title_sort | predictive online transient stability assessment for enhancing efficiency |
| topic | Classification generative adversarial networks phasor measurement unit transient stability |
| url | https://ieeexplore.ieee.org/document/10510341/ |
| work_keys_str_mv | AT ruima predictiveonlinetransientstabilityassessmentforenhancingefficiency AT saraeftekharnejad predictiveonlinetransientstabilityassessmentforenhancingefficiency AT chenzhong predictiveonlinetransientstabilityassessmentforenhancingefficiency |