Academic Journal
Power Quality Data Augmentation and Processing Method for Distribution Terminals Considering High-Frequency Sampling.
| Τίτλος: | Power Quality Data Augmentation and Processing Method for Distribution Terminals Considering High-Frequency Sampling. |
|---|---|
| Συγγραφείς: | Zeng, Ruijiang, Li, Zhiyong, Liu, Haodong, Che, Wenxuan, Yang, Jiamu, Li, Sifeng, Sun, Zhongwei |
| Πηγή: | Energies (19961073); Dec2025, Vol. 18 Issue 24, p6426, 25p |
| Θεματικοί όροι: | Data augmentation, Signal processing, Outlier detection, Power supply quality, Convolutional neural networks, Electronic data processing, Electric power distribution grids |
| Περίληψη: | The safe and stable operation of distribution networks relies on the real-time monitoring, analysis, and feedback of power quality data. However, with the continuous advancement of distribution network construction, the number of distributed power electronic devices has increased significantly, leading to frequent power quality issues such as voltage fluctuations, harmonic pollution, and three-phase unbalance in distribution terminals. Therefore, the augmentation and processing of power quality data have become crucial for ensuring the stable operation of distribution networks. Traditional methods for augmenting and processing power quality data fail to consider the differentiated characteristics of burrs in signal sequences and neglect the comprehensive consideration of both time-domain and frequency-domain features in disturbance identification. This results in the distortion of high-frequency fault information, and insufficient robustness and accuracy in identifying Power Quality Disturbance (PQD) against the complex noise background of distribution networks. In response to these issues, we propose a power quality data augmentation and processing method for distribution terminals considering high-frequency sampling. Firstly, a burr removal method of the sampling waveform based on a high-frequency filter operator is proposed. By comprehensively considering the characteristics of concavity and convexity in both burr and normal waveforms, a high-frequency filtering operator is introduced. Additional constraints and parameters are applied to suppress sequences with burr characteristics, thereby accurately eliminating burrs while preserving the key features of valid information. This approach avoids distortion of high-frequency fault information after filtering, which supports subsequent PQD identification. Secondly, a PQD identification method based on a dual-channel time–frequency feature fusion network is proposed. The PQD signals undergo an S-transform and period reconfiguration to construct matrix image features in the time–frequency domain. Finally, these features are input into a Convolutional Neural Network (CNN) and a Transformer encoder to extract highly coupled global features, which are then fused through a cross-attention mechanism. The identification results of PQD are output through a classification layer, thereby enhancing the robustness and accuracy of disturbance identification against the complex noise background of distribution networks. Simulation results demonstrate that the proposed algorithm achieves optimal burr removal and disturbance identification accuracy. [ABSTRACT FROM AUTHOR] |
| Copyright of Energies (19961073) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edb&genre=article&issn=19961073&ISBN=&volume=18&issue=24&date=20251215&spage=6426&pages=6426-6450&title=Energies (19961073)&atitle=Power%20Quality%20Data%20Augmentation%20and%20Processing%20Method%20for%20Distribution%20Terminals%20Considering%20High-Frequency%20Sampling.&aulast=Zeng%2C%20Ruijiang&id=DOI:10.3390/en18246426 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
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| Items | – Name: Title Label: Title Group: Ti Data: Power Quality Data Augmentation and Processing Method for Distribution Terminals Considering High-Frequency Sampling. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zeng%2C+Ruijiang%22">Zeng, Ruijiang</searchLink><br /><searchLink fieldCode="AR" term="%22Li%2C+Zhiyong%22">Li, Zhiyong</searchLink><br /><searchLink fieldCode="AR" term="%22Liu%2C+Haodong%22">Liu, Haodong</searchLink><br /><searchLink fieldCode="AR" term="%22Che%2C+Wenxuan%22">Che, Wenxuan</searchLink><br /><searchLink fieldCode="AR" term="%22Yang%2C+Jiamu%22">Yang, Jiamu</searchLink><br /><searchLink fieldCode="AR" term="%22Li%2C+Sifeng%22">Li, Sifeng</searchLink><br /><searchLink fieldCode="AR" term="%22Sun%2C+Zhongwei%22">Sun, Zhongwei</searchLink> – Name: TitleSource Label: Source Group: Src Data: Energies (19961073); Dec2025, Vol. 18 Issue 24, p6426, 25p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Data+augmentation%22">Data augmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+processing%22">Signal processing</searchLink><br /><searchLink fieldCode="DE" term="%22Outlier+detection%22">Outlier detection</searchLink><br /><searchLink fieldCode="DE" term="%22Power+supply+quality%22">Power supply quality</searchLink><br /><searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+data+processing%22">Electronic data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+power+distribution+grids%22">Electric power distribution grids</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The safe and stable operation of distribution networks relies on the real-time monitoring, analysis, and feedback of power quality data. However, with the continuous advancement of distribution network construction, the number of distributed power electronic devices has increased significantly, leading to frequent power quality issues such as voltage fluctuations, harmonic pollution, and three-phase unbalance in distribution terminals. Therefore, the augmentation and processing of power quality data have become crucial for ensuring the stable operation of distribution networks. Traditional methods for augmenting and processing power quality data fail to consider the differentiated characteristics of burrs in signal sequences and neglect the comprehensive consideration of both time-domain and frequency-domain features in disturbance identification. This results in the distortion of high-frequency fault information, and insufficient robustness and accuracy in identifying Power Quality Disturbance (PQD) against the complex noise background of distribution networks. In response to these issues, we propose a power quality data augmentation and processing method for distribution terminals considering high-frequency sampling. Firstly, a burr removal method of the sampling waveform based on a high-frequency filter operator is proposed. By comprehensively considering the characteristics of concavity and convexity in both burr and normal waveforms, a high-frequency filtering operator is introduced. Additional constraints and parameters are applied to suppress sequences with burr characteristics, thereby accurately eliminating burrs while preserving the key features of valid information. This approach avoids distortion of high-frequency fault information after filtering, which supports subsequent PQD identification. Secondly, a PQD identification method based on a dual-channel time–frequency feature fusion network is proposed. The PQD signals undergo an S-transform and period reconfiguration to construct matrix image features in the time–frequency domain. Finally, these features are input into a Convolutional Neural Network (CNN) and a Transformer encoder to extract highly coupled global features, which are then fused through a cross-attention mechanism. The identification results of PQD are output through a classification layer, thereby enhancing the robustness and accuracy of disturbance identification against the complex noise background of distribution networks. Simulation results demonstrate that the proposed algorithm achieves optimal burr removal and disturbance identification accuracy. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Energies (19961073) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/en18246426 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 25 StartPage: 6426 Subjects: – SubjectFull: Data augmentation Type: general – SubjectFull: Signal processing Type: general – SubjectFull: Outlier detection Type: general – SubjectFull: Power supply quality Type: general – SubjectFull: Convolutional neural networks Type: general – SubjectFull: Electronic data processing Type: general – SubjectFull: Electric power distribution grids Type: general Titles: – TitleFull: Power Quality Data Augmentation and Processing Method for Distribution Terminals Considering High-Frequency Sampling. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zeng, Ruijiang – PersonEntity: Name: NameFull: Li, Zhiyong – PersonEntity: Name: NameFull: Liu, Haodong – PersonEntity: Name: NameFull: Che, Wenxuan – PersonEntity: Name: NameFull: Yang, Jiamu – PersonEntity: Name: NameFull: Li, Sifeng – PersonEntity: Name: NameFull: Sun, Zhongwei IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 12 Text: Dec2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 19961073 Numbering: – Type: volume Value: 18 – Type: issue Value: 24 Titles: – TitleFull: Energies (19961073) Type: main |
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