Partial Discharge Signal Extraction Method Based on EDSSV and Low Rank RBF Neural Network Article Swipe
Xiaoli Yang
,
Huang Hong-guang
,
Qin Shu
,
Dakun Zhang
,
Bojian Chen
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/access.2021.3049435
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/access.2021.3049435
The detection process of partial discharge (PD) ultra-high frequency (UHF) signal is easily affected by white noise and periodic narrowband noise, which hinder the fault diagnosis of high-voltage electrical appliances. In order to extract PD UHF signal and suppress noise effectively, this paper provides a new method to detect PD UHF signal by EDSSV and low rank RBF neural network. Firstly, the singular value decomposition (SVD) is performed on the mixed noises of PD signal. Secondly, the peak index of energy difference spectrum of singular value (EDSSV) is selected as optimal singular value threshold, and then the periodic narrowband noise is eliminated by reconstructing the effective rank order. Finally, radial basis function (RBF) neural network is used to approximate the denoised PD signal, and Gaussian window filter is used to extract the PD signal. To verify the performance of the proposed method, we compared it with other three algorithms in simulation and field detection, including adaptive singular value decomposition (ASVD), singular value decomposition based on S-transform and MTFM (S-SVD) and EMD-WT algorithms. Particularly, four evaluation indices are designed for the detection data, which consider both the noise suppression and feature preservation. The results demonstrate the validity of the proposed method with higher signal-to-noise ratio and less waveform distortion.
Related Topics
Concepts
Singular value decomposition
Narrowband
SIGNAL (programming language)
Noise (video)
Computer science
Algorithm
Singular spectrum analysis
Additive white Gaussian noise
Gaussian noise
Waveform
Artificial neural network
Partial discharge
Pattern recognition (psychology)
White noise
Artificial intelligence
Engineering
Telecommunications
Voltage
Image (mathematics)
Programming language
Electrical engineering
Radar
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2021.3049435
- https://ieeexplore.ieee.org/ielx7/6287639/9312710/09314031.pdf
- OA Status
- gold
- Cited By
- 15
- References
- 21
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3120030271
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3120030271Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/access.2021.3049435Digital Object Identifier
- Title
-
Partial Discharge Signal Extraction Method Based on EDSSV and Low Rank RBF Neural NetworkWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-01-01Full publication date if available
- Authors
-
Xiaoli Yang, Huang Hong-guang, Qin Shu, Dakun Zhang, Bojian ChenList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2021.3049435Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/6287639/9312710/09314031.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://ieeexplore.ieee.org/ielx7/6287639/9312710/09314031.pdfDirect OA link when available
- Concepts
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Singular value decomposition, Narrowband, SIGNAL (programming language), Noise (video), Computer science, Algorithm, Singular spectrum analysis, Additive white Gaussian noise, Gaussian noise, Waveform, Artificial neural network, Partial discharge, Pattern recognition (psychology), White noise, Artificial intelligence, Engineering, Telecommunications, Voltage, Image (mathematics), Programming language, Electrical engineering, RadarTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
15Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 4, 2023: 3, 2022: 6, 2021: 1Per-year citation counts (last 5 years)
- References (count)
-
21Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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