Fault detection method of digital three-dimensional substation based on singular value decomposition
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Guoyun Song
and
Ye Yuan
Abstract
In the process of operation and maintenance of digital three-dimensional substation, affected by the fault feature extraction method, the anti-interference ability of the detection method is poor. Therefore, a digital three-dimensional substation fault detection method based on singular value decomposition is proposed. According to the digital twin five-dimensional concept, a three-dimensional management and control model of the substation is built, the real-time collected current signal is denoised, and then a new feature extraction method is designed by using the singular value decomposition theory, and the characteristic parameters of the fault signal are defined. Finally, the maximum wavelet singular value is calculated by combining the wavelet transformation method and the singular value decomposition principle. The fault detection is completed according to the mutation of singular value. The simulation experiment results show that the proposed fault detection method has improved the anti-interference ability by 44 and 86%. It is verified that the method proposed in this study has good fault detection effect, and provides auxiliary decision-making basis for relevant departments.
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Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Research funding: None declared.
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Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
References
1. Sidhu, TS, Yin, Y. Modelling and Simulation for Performance Evaluation of IEC61850-Based Substation Communication Systems. IEEE Transactions on Power Delivery 2007;22:1482–9. https://ieeexplore.ieee.org/document/4265725.10.1109/TPWRD.2006.886788Search in Google Scholar
2. Li, HJ, Dai, FC, Li, WC. Stability assessment of a slope under a transformer substation using numerical modelling. Bulletin of Engineering Geology & the Environment 2021;70:385–94. https://link.springer.com/article/10.1007/s10064-010-0318-7.10.1007/s10064-010-0318-7Search in Google Scholar
3. Awrangjeb, M, Lu, G. An Improved Curvature Scale-Space Corner Detector and a Robust Corner Matching Approach for Transformed Image Identification. IEEE Transactions on Image Processing 2008;17:2425–41. https://ieeexplore.ieee.org/document/4664620.10.1109/TIP.2008.2006441Search in Google Scholar
4. Hill, JL, Culler, DE. Mica: A Wireless Platform for Deeply Embedded Networks. Micro IEEE 2022;22:12–24. https://ieeexplore.ieee.org/document/1134340.10.1109/MM.2002.1134340Search in Google Scholar
5. Lee, ES, Kim, JD, Kim, NH. Plunge grinding characteristics using the current signal of spindle motor. Materials Processing Technology 2003;132:58–66. http://www.onacademic.com/detail/journal_1000034205617810_08be.html.10.1016/S0924-0136(02)00261-3Search in Google Scholar
6. Pan, TC, Lee, CL. Application of wavelet theory to identify yielding in seismic response of bi‐linear structures. Earthquake Engineering & Structural Dynamics 2010;31:379–98. https://onlinelibrary.wiley.com/doi/pdf/10.1002/eqe.113.10.1002/eqe.113Search in Google Scholar
7. Mort, GS, Drennan, J. Mobile digital technology: Emerging issue for marketing. Journal of Database Marketing & Customer Strategy Management 2002;10:9–23. https://link.springer.com/article/10.1057/palgrave.jdm.3240090.10.1057/palgrave.jdm.3240090Search in Google Scholar
8. Vijayakumaran, S, Wong, TF. A search strategy for ultra-wideband signal acquisition. IEEE Transactions on Communications 2019;53:2015–9. https://ieeexplore.ieee.org/document/1556827/similar#similar.10.1109/TCOMM.2005.860074Search in Google Scholar
9. Martinis, J, Clarke, J. Measurements of current noise in DC SQUIDS. IEEE Transactions on Magnetics 2003;19:446–8. https://ieeexplore.ieee.org/document/1062411?arnumber=1062411.10.1109/TMAG.1983.1062411Search in Google Scholar
10. Thompson, OE, Tripputi, MT. NWP-Initialized Satellite Temperature Retrievals Using Statistical Regularization and Singular Value Decomposition Methods. Monthly Weather Review 2009;122:897. https://ui.adsabs.harvard.edu/abs/1994MWRv..122..897T/.10.1175/1520-0493(1994)122<0897:NISTRU>2.0.CO;2Search in Google Scholar
11. Drineas, P, Frieze, A, Kannan, R. Clustering Large Graphs via the Singular Value Decomposition. Machine Learning 2004;56:9–33. https://link.springer.com/article/10.1023/B:MACH.0000033113.59016.96.10.1023/B:MACH.0000033113.59016.96Search in Google Scholar
12. Bihan, NL, Mars, J. Singular value decomposition of quaternion matrices: a new tool for vector-sensor signal processing. Signal Processing 2008;84:1177–99. https://www.sciencedirect.com/science/article/abs/pii/S0165168404000647?via%3Dihub.10.1016/j.sigpro.2004.04.001Search in Google Scholar
13. Berkant, Savas. Handwritten digit classification using higher order singular value decomposition. Pattern Recognition 2007;40:993–1003. https://www.sciencedirect.com/science/article/abs/pii/S0031320306003542.10.1016/j.patcog.2006.08.004Search in Google Scholar
14. Debnath, L, Antoine, JP. Wavelet Transforms and Their Applications. Physics Today 2003;56:68. https://www.researchgate.net/publication/238958371.10.1063/1.1580056Search in Google Scholar
15. Lee, DU, Kim, LW, Villasenor, JD. Precision-Aware Self-Quantizing Hardware Architectures for the Discrete Wavelet Transform. IEEE Transactions on Image Processing 2012;21:768–77. https://ieeexplore.ieee.org/document/5971788.10.1109/TIP.2011.2163519Search in Google Scholar PubMed
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Articles in the same Issue
- Frontmatter
- Research Articles
- Accounting for current limitation and input saturation in adaptive nonlinear control of fuel cell power system
- Day-ahead and real-time congestion scheduling method for distribution network with multiple access to electric vehicle charging piles
- A real-time hybrid battery state of charge and state of health estimation technique in renewable energy integrated microgrid applications
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- Efficiency analysis of dual motor powertrain with planetary gear set
- Information model of low-voltage distribution IoT monitoring terminal based on IEC 61850
- Most Valuable Player based selective harmonic elimination in a cascaded H-bridge inverter for wide operating range
- A new reduced switch double boost five-level inverter with Self-Balancing of Capacitor Voltage
- Voltage control of standalone photovoltaic – electrolyzer- fuel cell-battery energy system
- Bad data identification and fault diagnosis of smart substation based on secondary system information redundancy
- Fault detection method of digital three-dimensional substation based on singular value decomposition
- Blockchain data privacy protection modeling based on CP-ABE algorithm
