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Cloud-Edge Collabor...
Cloud-Edge Collaboration-Based Local Voltage Control for DGs With Privacy Preservation
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- Zhao, Jinli (författare)
- Tianjin Univ, Key Lab Smart Grid, Minist Educ, Tianjin 300072, Peoples R China.
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- Zhang, Ziqi (författare)
- Tianjin Univ, Key Lab Smart Grid, Minist Educ, Tianjin 300072, Peoples R China.
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- Yu, Hao (författare)
- Tianjin Univ, Key Lab Smart Grid, Minist Educ, Tianjin 300072, Peoples R China.
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- Ji, Haoran (författare)
- Tianjin Univ, Key Lab Smart Grid, Minist Educ, Tianjin 300072, Peoples R China.
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- Li, Peng (författare)
- Tianjin Univ, Key Lab Smart Grid, Minist Educ, Tianjin 300072, Peoples R China.
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- Xi, Wei (författare)
- Tianjin Univ, Key Lab Smart Grid, Minist Educ, Tianjin 300072, Peoples R China.;China Southern Power Grid, Digital Grid Res Inst, Guangzhou 510670, Peoples R China.
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- Yan, Jinyue, 1959- (författare)
- Mälardalens universitet,Framtidens energi
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- Wang, Chengshan (författare)
- Tianjin Univ, Key Lab Smart Grid, Minist Educ, Tianjin 300072, Peoples R China.
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Tianjin Univ, Key Lab Smart Grid, Minist Educ, Tianjin 300072, Peoples R China Tianjin Univ, Key Lab Smart Grid, Minist Educ, Tianjin 300072, Peoples R China.;China Southern Power Grid, Digital Grid Res Inst, Guangzhou 510670, Peoples R China. (creator_code:org_t)
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2023
- 2023
- Engelska.
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Ingår i: IEEE Transactions on Industrial Informatics. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 1551-3203 .- 1941-0050. ; 19:1, s. 98-108
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- The increased distributed generators (DGs) have exacerbated voltage violations in active distribution networks (ADNs). Local reactive power control of DG inverters can realize a fast response to frequent voltage fluctuations. However, commonly used model-based voltage control depends upon accurate network parameters and entire ADN data, which may cause the sensitive information leakage of ADN and DG behaviors in practical operation. In this article, a cloud-edge collaboration-based local voltage control strategy for DGs is proposed with privacy preservation. First, a local voltage control framework is established based on cloud-edge collaboration, in which a surrogate model is built based on the graph convolutional neural networks to estimate the ADN voltages. By transferring the surrogate model, the edge side can obtain the exact voltage estimation in the local curve tuning process without the authority of the whole ADN data, preserving the network parameters of ADN. Then, the interarea coordination based on federated learning is proposed to realize the parameter updating of DG control curves, which can achieve better voltage control performance. By updating surrogate submodels based on private data distributed across multiple edge devices, federated learning can effectively preserve DG behaviors. Finally, the effectiveness and adaptability of the proposed control strategy are validated using the modified IEEE 33-node system. The proposed local DG control strategy can effectively cope with voltage problems and enhance the adaptability to variations in practical operation states while considering privacy preservation.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Maskinteknik -- Energiteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Mechanical Engineering -- Energy Engineering (hsv//eng)
Nyckelord
- Voltage control
- Training
- Collaboration
- Privacy
- Fluctuations
- Data models
- Voltage fluctuations
- Active distribution network (ADN)
- cloud-edge collaboration
- distributed generator (DG)
- federated learning
- graph convolutional neural networks (GCNs)
- local voltage control
- privacy preservation
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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