Resnet-based power system frequency security assessment considering frequency spatiotemporal distribution characteristics
Under the background of the accelerated transformation of the modern power system, renewable energy will gradually replace the role of traditional energy in the power system. Because of the randomness, uncertainty, and low inertia of renewable energy, the power system frequency security assessment i...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-10-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S235248472300820X |
| _version_ | 1797388787265830912 |
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| author | Yanbo Wang Junyong Wu |
| author_facet | Yanbo Wang Junyong Wu |
| author_sort | Yanbo Wang |
| collection | DOAJ |
| description | Under the background of the accelerated transformation of the modern power system, renewable energy will gradually replace the role of traditional energy in the power system. Because of the randomness, uncertainty, and low inertia of renewable energy, the power system frequency security assessment is becoming a serious issue under the influence of large-scale renewable energy integrations. To achieve the rapid assessment of power system frequency security, a resnet-based frequency security assessment method considering frequency spatiotemporal distribution characteristics is proposed. In this paper, to provide more comprehensive frequency response information, the spatiotemporal characteristics of frequency are introduced and integrated into the input feature set. At the same time, the Resnet network and multi-task-learning framework are applied to construct a frequency security assessment model. Based on the experimental results on the modified IEEE39 bus system, the assessment accuracy is higher than 99%, which shows the excellent assessment performance of the proposed model. |
| first_indexed | 2024-03-08T22:45:53Z |
| format | Article |
| id | doaj.art-02c63900e3d544a5a98e4089bc312302 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-03-08T22:45:53Z |
| publishDate | 2023-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-02c63900e3d544a5a98e4089bc3123022023-12-17T06:38:40ZengElsevierEnergy Reports2352-48472023-10-019125134Resnet-based power system frequency security assessment considering frequency spatiotemporal distribution characteristicsYanbo Wang0Junyong Wu1Corresponding author.; School of electrical engineering, Beijing Jiaotong University, Beijing, 100044, ChinaSchool of electrical engineering, Beijing Jiaotong University, Beijing, 100044, ChinaUnder the background of the accelerated transformation of the modern power system, renewable energy will gradually replace the role of traditional energy in the power system. Because of the randomness, uncertainty, and low inertia of renewable energy, the power system frequency security assessment is becoming a serious issue under the influence of large-scale renewable energy integrations. To achieve the rapid assessment of power system frequency security, a resnet-based frequency security assessment method considering frequency spatiotemporal distribution characteristics is proposed. In this paper, to provide more comprehensive frequency response information, the spatiotemporal characteristics of frequency are introduced and integrated into the input feature set. At the same time, the Resnet network and multi-task-learning framework are applied to construct a frequency security assessment model. Based on the experimental results on the modified IEEE39 bus system, the assessment accuracy is higher than 99%, which shows the excellent assessment performance of the proposed model.http://www.sciencedirect.com/science/article/pii/S235248472300820XDeep learningFrequency securityPower systemMulti-task-learning |
| spellingShingle | Yanbo Wang Junyong Wu Resnet-based power system frequency security assessment considering frequency spatiotemporal distribution characteristics Energy Reports Deep learning Frequency security Power system Multi-task-learning |
| title | Resnet-based power system frequency security assessment considering frequency spatiotemporal distribution characteristics |
| title_full | Resnet-based power system frequency security assessment considering frequency spatiotemporal distribution characteristics |
| title_fullStr | Resnet-based power system frequency security assessment considering frequency spatiotemporal distribution characteristics |
| title_full_unstemmed | Resnet-based power system frequency security assessment considering frequency spatiotemporal distribution characteristics |
| title_short | Resnet-based power system frequency security assessment considering frequency spatiotemporal distribution characteristics |
| title_sort | resnet based power system frequency security assessment considering frequency spatiotemporal distribution characteristics |
| topic | Deep learning Frequency security Power system Multi-task-learning |
| url | http://www.sciencedirect.com/science/article/pii/S235248472300820X |
| work_keys_str_mv | AT yanbowang resnetbasedpowersystemfrequencysecurityassessmentconsideringfrequencyspatiotemporaldistributioncharacteristics AT junyongwu resnetbasedpowersystemfrequencysecurityassessmentconsideringfrequencyspatiotemporaldistributioncharacteristics |