Power system stability assessment method based on GAN and GRU‐Attention using incomplete voltage data
Abstract The social economy is growing rapidly, and the power grid load demand is increasing. To maintain the stability of the power grid, it is crucial to achieve accurate and rapid power system stability assessment. In the actual operation of the power network, data loss is an unavoidable situatio...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-08-01
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| Series: | IET Generation, Transmission & Distribution |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/gtd2.12925 |
| _version_ | 1797742356541210624 |
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| author | Xuan Deng Yufan Hu Yiyang Jia Mao Peng |
| author_facet | Xuan Deng Yufan Hu Yiyang Jia Mao Peng |
| author_sort | Xuan Deng |
| collection | DOAJ |
| description | Abstract The social economy is growing rapidly, and the power grid load demand is increasing. To maintain the stability of the power grid, it is crucial to achieve accurate and rapid power system stability assessment. In the actual operation of the power network, data loss is an unavoidable situation. However, most of the data‐driven models currently used assume that the input data is complete, which has obvious limitations in real‐world applications. This paper suggests an IVS‐GAN model to assess power system stability using incomplete phasor measurement unit measurement data with random loss. The proposed method combines the super‐resolution perception technology based on generative adversarial network (GAN) with a time‐series signal classification model. The generator adopts a 1D U‐Net network and uses convolutional layers to complete and recover missing data. The discriminator adopts a new gated recurrent unit–attention architecture proposed here to better extract voltage temporal variation features on key buses. The result of this paper is that the stability evaluation method outperforms other algorithms in high voltage data loss rates on the New England 10‐machine 39‐bus system. |
| first_indexed | 2024-03-12T14:39:41Z |
| format | Article |
| id | doaj.art-0257943a225c4a0f95d4f28d779b63a8 |
| institution | Directory Open Access Journal |
| issn | 1751-8687 1751-8695 |
| language | English |
| last_indexed | 2024-03-12T14:39:41Z |
| publishDate | 2023-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Generation, Transmission & Distribution |
| spelling | doaj.art-0257943a225c4a0f95d4f28d779b63a82023-08-16T10:15:34ZengWileyIET Generation, Transmission & Distribution1751-86871751-86952023-08-0117163692370510.1049/gtd2.12925Power system stability assessment method based on GAN and GRU‐Attention using incomplete voltage dataXuan Deng0Yufan Hu1Yiyang Jia2Mao Peng3School of Mechanical and Electrical EngineeringUniversity of Electronic Science and Technology of ChinaChengduSichuanChinaSchool of Mechanical and Electrical EngineeringUniversity of Electronic Science and Technology of ChinaChengduSichuanChinaSchool of Mechanical and Electrical EngineeringUniversity of Electronic Science and Technology of ChinaChengduSichuanChinaSchool of Mechanical and Electrical EngineeringUniversity of Electronic Science and Technology of ChinaChengduSichuanChinaAbstract The social economy is growing rapidly, and the power grid load demand is increasing. To maintain the stability of the power grid, it is crucial to achieve accurate and rapid power system stability assessment. In the actual operation of the power network, data loss is an unavoidable situation. However, most of the data‐driven models currently used assume that the input data is complete, which has obvious limitations in real‐world applications. This paper suggests an IVS‐GAN model to assess power system stability using incomplete phasor measurement unit measurement data with random loss. The proposed method combines the super‐resolution perception technology based on generative adversarial network (GAN) with a time‐series signal classification model. The generator adopts a 1D U‐Net network and uses convolutional layers to complete and recover missing data. The discriminator adopts a new gated recurrent unit–attention architecture proposed here to better extract voltage temporal variation features on key buses. The result of this paper is that the stability evaluation method outperforms other algorithms in high voltage data loss rates on the New England 10‐machine 39‐bus system.https://doi.org/10.1049/gtd2.12925feature extractiongenerative adversarial networkincomplete signalpower systemstability assessment |
| spellingShingle | Xuan Deng Yufan Hu Yiyang Jia Mao Peng Power system stability assessment method based on GAN and GRU‐Attention using incomplete voltage data IET Generation, Transmission & Distribution feature extraction generative adversarial network incomplete signal power system stability assessment |
| title | Power system stability assessment method based on GAN and GRU‐Attention using incomplete voltage data |
| title_full | Power system stability assessment method based on GAN and GRU‐Attention using incomplete voltage data |
| title_fullStr | Power system stability assessment method based on GAN and GRU‐Attention using incomplete voltage data |
| title_full_unstemmed | Power system stability assessment method based on GAN and GRU‐Attention using incomplete voltage data |
| title_short | Power system stability assessment method based on GAN and GRU‐Attention using incomplete voltage data |
| title_sort | power system stability assessment method based on gan and gru attention using incomplete voltage data |
| topic | feature extraction generative adversarial network incomplete signal power system stability assessment |
| url | https://doi.org/10.1049/gtd2.12925 |
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