Data-Driven Voltage Control Method of Active Distribution Networks Based on Koopman Operator Theory Article Swipe
Zhaobin Du
,
Xiaoke Lin
,
Guoduan Zhong
,
Hao Liu
,
W. R. Zhao
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/math12243944
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/math12243944
The advent of large-scale distributed generation (DG) has introduced several challenges to the voltage control of active distribution networks (ADNs). These challenges include the heterogeneity of control devices, the complexity of models, and their inherent fluctuations. To maintain ADN voltage stability more economically and quickly, a data-driven ADN voltage control scheme is proposed in this paper. Firstly, based on the multi-run state sensitivity matrix, buses with similar voltage responses are clustered, and critical buses are selected to downsize the scale of the model. Secondly, a linear voltage-to-power dynamics model in high-dimensional state space is trained based on the offline data of critical bus voltages, DGs, and energy storage system (ESS) outputs, utilizing the Koopman theory and the Extended Dynamic Mode Decomposition (EDMD) method. A linear model predictive voltage controller, which takes ADN stability and control cost into account, is also proposed. Finally, the effectiveness and applicability of the method are verified by applying it to an improved 33-bus ADN system. The proposed control method can respond more quickly and accurately to the voltage fluctuation problems caused by source-load disturbances and short-circuit faults.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/math12243944
- https://www.mdpi.com/2227-7390/12/24/3944/pdf?version=1734329779
- OA Status
- gold
- Cited By
- 2
- References
- 32
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405424671
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https://doi.org/10.3390/math12243944Digital Object Identifier
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Data-Driven Voltage Control Method of Active Distribution Networks Based on Koopman Operator TheoryWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-12-15Full publication date if available
- Authors
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Zhaobin Du, Xiaoke Lin, Guoduan Zhong, Hao Liu, W. R. ZhaoList of authors in order
- Landing page
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https://doi.org/10.3390/math12243944Publisher landing page
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https://www.mdpi.com/2227-7390/12/24/3944/pdf?version=1734329779Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2227-7390/12/24/3944/pdf?version=1734329779Direct OA link when available
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Operator (biology), Control theory (sociology), Computer science, Control (management), Distribution (mathematics), Voltage, Mathematics, Artificial intelligence, Engineering, Mathematical analysis, Electrical engineering, Chemistry, Gene, Repressor, Transcription factor, BiochemistryTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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