Data-Driven Frequency Security Assessment Based on Generative Adversarial Networks and Metric Learning

Li Huarui; Zhu Xinyao; Jia Yongyong; Li Zheng; Wang Xiaobo

doi:doi:10.11648/j.frontiers.20250501.12

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Data-Driven Frequency Security Assessment Based on Generative Adversarial Networks and Metric Learning

Li Huarui^*

, Zhu Xinyao, Jia Yongyong, Li Zheng

, Wang Xiaobo

Published in Frontiers (Volume 5, Issue 1)

Received: 31 October 2024 Accepted: 4 January 2025 Published: 24 January 2025

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Abstract

With construction of large-capacity direct current transmission projects and large-scale integration of renewable energy, frequency security of the power system is facing severe challenges. For fast and accurate online assessment of frequency security, a data-driven frequency security assessment model based on Generative Adversarial Network (GAN) and Metric Learning (ML) is proposed in this paper. Firstly, the key frequency security indicators are selected as the outputs of the model, and the input feature set is constructed. Then, distribution information of historical operation scenarios is learned through Wasserstein Generative Adversarial Network (WGAN), in order to generate operation scenarios covering typical operation modes for training sample set establishment. The generated operation scenarios are adjusted based on rejection sampling and resampling techniques, in order to increase the density of training samples near key scenes. Finally, considering inapplicability of a single assessment model for frequency security assessment in power systems with complicated changes of operation conditions, a combined assessment model for frequency security assessment composed of multiple sub-models is constructed based on Metric Learning for Kernel Regression (MLKR). The original distance metric is adjusted with metric learning techniques to make samples with similar frequency dynamics close. Then the samples with similar frequency dynamics are clustered into the same cluster, and the corresponding sub-model is established. A simplified Shandong power system example is used to verify the effectiveness of the proposed method.

Published in	Frontiers (Volume 5, Issue 1)
DOI	10.11648/j.frontiers.20250501.12
Page(s)	30-41
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2025. Published by Science Publishing Group

Keywords

Frequency Security, Machine Learning, Data-Driven, Generative Adversarial Network, Metric Learning

References

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APA Style

Huarui, L., Xinyao, Z., Yongyong, J., Zheng, L., Xiaobo, W. (2025). Data-Driven Frequency Security Assessment Based on Generative Adversarial Networks and Metric Learning. Frontiers, 5(1), 30-41. https://doi.org/10.11648/j.frontiers.20250501.12

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ACS Style

Huarui, L.; Xinyao, Z.; Yongyong, J.; Zheng, L.; Xiaobo, W. Data-Driven Frequency Security Assessment Based on Generative Adversarial Networks and Metric Learning. Frontiers. 2025, 5(1), 30-41. doi: 10.11648/j.frontiers.20250501.12

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AMA Style

Huarui L, Xinyao Z, Yongyong J, Zheng L, Xiaobo W. Data-Driven Frequency Security Assessment Based on Generative Adversarial Networks and Metric Learning. Frontiers. 2025;5(1):30-41. doi: 10.11648/j.frontiers.20250501.12

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@article{10.11648/j.frontiers.20250501.12,
  author = {Li Huarui and Zhu Xinyao and Jia Yongyong and Li Zheng and Wang Xiaobo},
  title = {Data-Driven Frequency Security Assessment Based on Generative Adversarial Networks and Metric Learning
},
  journal = {Frontiers},
  volume = {5},
  number = {1},
  pages = {30-41},
  doi = {10.11648/j.frontiers.20250501.12},
  url = {https://doi.org/10.11648/j.frontiers.20250501.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.frontiers.20250501.12},
  abstract = {With construction of large-capacity direct current transmission projects and large-scale integration of renewable energy, frequency security of the power system is facing severe challenges. For fast and accurate online assessment of frequency security, a data-driven frequency security assessment model based on Generative Adversarial Network (GAN) and Metric Learning (ML) is proposed in this paper. Firstly, the key frequency security indicators are selected as the outputs of the model, and the input feature set is constructed. Then, distribution information of historical operation scenarios is learned through Wasserstein Generative Adversarial Network (WGAN), in order to generate operation scenarios covering typical operation modes for training sample set establishment. The generated operation scenarios are adjusted based on rejection sampling and resampling techniques, in order to increase the density of training samples near key scenes. Finally, considering inapplicability of a single assessment model for frequency security assessment in power systems with complicated changes of operation conditions, a combined assessment model for frequency security assessment composed of multiple sub-models is constructed based on Metric Learning for Kernel Regression (MLKR). The original distance metric is adjusted with metric learning techniques to make samples with similar frequency dynamics close. Then the samples with similar frequency dynamics are clustered into the same cluster, and the corresponding sub-model is established. A simplified Shandong power system example is used to verify the effectiveness of the proposed method.
},
 year = {2025}
}

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TY  - JOUR
T1  - Data-Driven Frequency Security Assessment Based on Generative Adversarial Networks and Metric Learning

AU  - Li Huarui
AU  - Zhu Xinyao
AU  - Jia Yongyong
AU  - Li Zheng
AU  - Wang Xiaobo
Y1  - 2025/01/24
PY  - 2025
N1  - https://doi.org/10.11648/j.frontiers.20250501.12
DO  - 10.11648/j.frontiers.20250501.12
T2  - Frontiers
JF  - Frontiers
JO  - Frontiers
SP  - 30
EP  - 41
PB  - Science Publishing Group
SN  - 2994-7197
UR  - https://doi.org/10.11648/j.frontiers.20250501.12
AB  - With construction of large-capacity direct current transmission projects and large-scale integration of renewable energy, frequency security of the power system is facing severe challenges. For fast and accurate online assessment of frequency security, a data-driven frequency security assessment model based on Generative Adversarial Network (GAN) and Metric Learning (ML) is proposed in this paper. Firstly, the key frequency security indicators are selected as the outputs of the model, and the input feature set is constructed. Then, distribution information of historical operation scenarios is learned through Wasserstein Generative Adversarial Network (WGAN), in order to generate operation scenarios covering typical operation modes for training sample set establishment. The generated operation scenarios are adjusted based on rejection sampling and resampling techniques, in order to increase the density of training samples near key scenes. Finally, considering inapplicability of a single assessment model for frequency security assessment in power systems with complicated changes of operation conditions, a combined assessment model for frequency security assessment composed of multiple sub-models is constructed based on Metric Learning for Kernel Regression (MLKR). The original distance metric is adjusted with metric learning techniques to make samples with similar frequency dynamics close. Then the samples with similar frequency dynamics are clustered into the same cluster, and the corresponding sub-model is established. A simplified Shandong power system example is used to verify the effectiveness of the proposed method.

VL  - 5
IS  - 1
ER  -

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Author Information

Li Huarui

State Grid Jiangsu Electric Power Company Limited, Research Institute, Nanjing, China

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http://orcid.org/0000-0002-4405-8365
Zhu Xinyao

State Grid Jiangsu Electric Power Company Limited, Research Institute, Nanjing, China

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Jia Yongyong

State Grid Jiangsu Electric Power Company Limited, Research Institute, Nanjing, China

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Li Zheng

State Grid Jiangsu Electric Power Company Limited, Research Institute, Nanjing, China

Contact Email

http://orcid.org/0000-0002-7922-5004
Wang Xiaobo

State Grid Jiangsu Electric Power Company Limited, Research Institute, Nanjing, China

Contact Email

http://orcid.org/0000-0002-4337-5254

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APA Style

Huarui, L., Xinyao, Z., Yongyong, J., Zheng, L., Xiaobo, W. (2025). Data-Driven Frequency Security Assessment Based on Generative Adversarial Networks and Metric Learning. Frontiers, 5(1), 30-41. https://doi.org/10.11648/j.frontiers.20250501.12

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ACS Style

Huarui, L.; Xinyao, Z.; Yongyong, J.; Zheng, L.; Xiaobo, W. Data-Driven Frequency Security Assessment Based on Generative Adversarial Networks and Metric Learning. Frontiers. 2025, 5(1), 30-41. doi: 10.11648/j.frontiers.20250501.12

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AMA Style

Huarui L, Xinyao Z, Yongyong J, Zheng L, Xiaobo W. Data-Driven Frequency Security Assessment Based on Generative Adversarial Networks and Metric Learning. Frontiers. 2025;5(1):30-41. doi: 10.11648/j.frontiers.20250501.12

Copy | Download

@article{10.11648/j.frontiers.20250501.12,
  author = {Li Huarui and Zhu Xinyao and Jia Yongyong and Li Zheng and Wang Xiaobo},
  title = {Data-Driven Frequency Security Assessment Based on Generative Adversarial Networks and Metric Learning
},
  journal = {Frontiers},
  volume = {5},
  number = {1},
  pages = {30-41},
  doi = {10.11648/j.frontiers.20250501.12},
  url = {https://doi.org/10.11648/j.frontiers.20250501.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.frontiers.20250501.12},
  abstract = {With construction of large-capacity direct current transmission projects and large-scale integration of renewable energy, frequency security of the power system is facing severe challenges. For fast and accurate online assessment of frequency security, a data-driven frequency security assessment model based on Generative Adversarial Network (GAN) and Metric Learning (ML) is proposed in this paper. Firstly, the key frequency security indicators are selected as the outputs of the model, and the input feature set is constructed. Then, distribution information of historical operation scenarios is learned through Wasserstein Generative Adversarial Network (WGAN), in order to generate operation scenarios covering typical operation modes for training sample set establishment. The generated operation scenarios are adjusted based on rejection sampling and resampling techniques, in order to increase the density of training samples near key scenes. Finally, considering inapplicability of a single assessment model for frequency security assessment in power systems with complicated changes of operation conditions, a combined assessment model for frequency security assessment composed of multiple sub-models is constructed based on Metric Learning for Kernel Regression (MLKR). The original distance metric is adjusted with metric learning techniques to make samples with similar frequency dynamics close. Then the samples with similar frequency dynamics are clustered into the same cluster, and the corresponding sub-model is established. A simplified Shandong power system example is used to verify the effectiveness of the proposed method.
},
 year = {2025}
}

Copy | Download

TY  - JOUR
T1  - Data-Driven Frequency Security Assessment Based on Generative Adversarial Networks and Metric Learning

AU  - Li Huarui
AU  - Zhu Xinyao
AU  - Jia Yongyong
AU  - Li Zheng
AU  - Wang Xiaobo
Y1  - 2025/01/24
PY  - 2025
N1  - https://doi.org/10.11648/j.frontiers.20250501.12
DO  - 10.11648/j.frontiers.20250501.12
T2  - Frontiers
JF  - Frontiers
JO  - Frontiers
SP  - 30
EP  - 41
PB  - Science Publishing Group
SN  - 2994-7197
UR  - https://doi.org/10.11648/j.frontiers.20250501.12
AB  - With construction of large-capacity direct current transmission projects and large-scale integration of renewable energy, frequency security of the power system is facing severe challenges. For fast and accurate online assessment of frequency security, a data-driven frequency security assessment model based on Generative Adversarial Network (GAN) and Metric Learning (ML) is proposed in this paper. Firstly, the key frequency security indicators are selected as the outputs of the model, and the input feature set is constructed. Then, distribution information of historical operation scenarios is learned through Wasserstein Generative Adversarial Network (WGAN), in order to generate operation scenarios covering typical operation modes for training sample set establishment. The generated operation scenarios are adjusted based on rejection sampling and resampling techniques, in order to increase the density of training samples near key scenes. Finally, considering inapplicability of a single assessment model for frequency security assessment in power systems with complicated changes of operation conditions, a combined assessment model for frequency security assessment composed of multiple sub-models is constructed based on Metric Learning for Kernel Regression (MLKR). The original distance metric is adjusted with metric learning techniques to make samples with similar frequency dynamics close. Then the samples with similar frequency dynamics are clustered into the same cluster, and the corresponding sub-model is established. A simplified Shandong power system example is used to verify the effectiveness of the proposed method.

VL  - 5
IS  - 1
ER  -

Copy | Download