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Recent Advances in Electrical & Electronic Engineering

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ISSN (Print): 2352-0965
ISSN (Online): 2352-0973

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Research Article

Research on Extraction and Integration Method of Core Feature of Power Transformer under Incomplete Information

Author(s): Jun Zhao, Shuguo Gao, Kun Meng*, Weiying Wang, Gang Li and Tao Zhao

Volume 15, Issue 6, 2022

Published on: 23 August, 2022

Page: [475 - 484] Pages: 10

DOI: 10.2174/2352096515666220817102738

Price: $65

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Abstract

Background: The digital transformation strategy puts forward higher requirements for the information support system of the power system. Also, it points out the direction for the operation and maintenance of essential equipment such as large power transformers. However, the complex operating environment of power transformers poses a challenge to the digital operation and maintenance of equipment.

Objectives: Complex power transformer monitoring data is characterized by a wide range of sources, complex structures, incomplete information and unfocused distribution. This paper discussed and studied the Extraction and Integration Method of Core Feature of Power Transformer under Incomplete Information to fully exploit the potential value of power data and realize data collection and condition assessment of the equipment.

Methods: First, the power transformer holographic data perception technology and its analysis methods were explained from the perspective of multiple parameters; Then, based on actual cases, an information decision table based on incomplete information of power transformers based on equipment components and fault types was established to obtain the core feature of the power transformer itself; Secondly, by analyzing the distribution of the core feature index, a model for multitask data integration and fusion in a cloud-edge-end architecture was proposed, and the integration method for multi-source data transfer was elaborated.

Results: Finally, the feasibility of the proposed method was verified through simulation analysis of a calculation example.

Conclusion: The method described in this article has a certain reference value for evaluating complex equipment that is mainly data-driven but lacks more serious information in the power grid digital transformation process.

Keywords: power transformer, condition assessment, multiparameter data, core feature, feature extraction, data integration.

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[1]
X. Bo, X. Han, H. Sun, and Y. Zhang, Condition-based maintenance optimization model for partially observable power equipment.Proceedings of the CSEE, vol. 38. 2018, pp. 4107-4116.
[2]
N. Dominelli, A. Rao, and P. Kundur, "Life extension and condition assessment: Techniques for an aging utility infrastructure", IEEE Power Energy Mag., vol. 4, no. 3, pp. 24-35, 2006.
[http://dx.doi.org/10.1109/MPAE.2006.1632451]
[3]
J. Ai, X. Dang, Q. Lv, C. Mei, and Y. Zhang, "Research on full dimension equipment status monitoring system with panoramic function", Power Syst. Prot. Control, vol. 47, no. 16, pp. 122-128, 2019.
[4]
Gang Li, B. Zhang, W. Zhao, Y. Liu, and S. Gao, "Data science issues in state evaluation of power equipment: Challenges and prospects", Autom. Electr. Power Syst., vol. 42, no. 21, pp. 10-20, 2018.
[5]
B. Qi, P. Zhang, S. Zhang, L. Zhao, B. Wang, M. Huang, Z. Tang, M. Ji, and C. Li, "Application status and development prospects of digital twin technology in condition assessment of power transmission and transformation equipment", High Volt. Eng., vol. 47, no. 5, pp. 1522-1538, 2021.
[6]
H. Wang, B. Wang, X. Dong, L. Yao, and F. Zhang, "Heterogeneous multiparameter feature-level fusion for multi-source power sensing terminals: Fusion mode, fusion framework and application scenarios", Trans. China Electrotech. Soc., vol. 36, no. 7, pp. 1314-1323, 2021.
[7]
H. Huang, Z. Xiong, and Y. Wang, "L. Leand J. Wang, “Research on multi-dimensional sensing and intelligent diagnosis system of substation equipment based on big data”", Power Syst. Big Data, vol. 22, no. 10, pp. 51-57, 2019.
[8]
Y. Xu, Y. Li, Y. Wang, B. Tao, L. Wang, and G. Zhang, "Early-warning methods of transformer conditions under information deficiency", High Volt. Eng., vol. 46, no. 9, pp. 3062-3069, 2020.
[9]
Guide for condition evaluation of oil-immersed power transformers (reactors): DL/T 1685-2017, 2017.
[10]
Guide for condition based maintenance strategy of oil-immersed power transformers (reactors): DL/T 1684-2017, 2017.
[11]
X. Zhang, D. Liu, B. Li, and C. Feng, "The concept of intelligent panoramic system and its application system in modern power grid", In Proc. of the CSEE, vol. 39, no. 10, pp. 2885-2895, 2019.
[12]
B. Zhang, H. Liu, G. Peng, M. Fan, S. Jia, and Y. Sun, “Operation state assessment and situation prediction of distribution transformer for cloud edge collaboration”, J. Chongqing Univ., 2021, pp. 1-13. Available from: http://kns.cnki.net/kcms/detail/50.1044.N.20210924.2138.002.html (Accessed on June 10, 2022).
[13]
L. Liu, and H. Li, "Function design and system implementation of edge computing gateway", Electr. Measur. Instrum., vol. 58, no. 8, pp. 42-48, 2021.
[14]
M. Kryszkiewicz, "Rough set approach to incomplete information systems", Inf. Sci., vol. 112, no. 1-4, pp. 39-49, 1998.
[http://dx.doi.org/10.1016/S0020-0255(98)10019-1]
[15]
Y. Xiao, J. Fan, and Y. Zhang, "How much shake can bolted joints take", Mach. Des., vol. 44, no. 18, pp. 122-128, 2021.
[16]
W. Huang, "Research development of particle swarm optimization algorithm", Comput. Eng. Softw., vol. 34, no. 4, pp. 73-77, 2014.
[17]
Y. Shi, and R. Eberhart, "A modified particle swarm optimizer", 2007 8th International Conference on Electronic Measurement and Instruments, vol. 2007, no. 2, pp. 675-679, 2007.
[18]
J. Wang, P. Zheng, Y. Lv, J. Bao, and J. Zhang, "Fog-IBDIS: Industrial big data integration and sharing with fog computing for manufacturing systems", Engineering, vol. 5, no. 4, pp. 662-670, 2019.
[http://dx.doi.org/10.1016/j.eng.2018.12.013]
[19]
H. Li, X. Wang, and Y. Guo, "Feature extraction and classification in smart grid data based on KL-divergence and KL transform", Electr. Meas. Instrum., vol. 56, no. 6, pp. 87-92, 2019.
[20]
B. Huang, X. Zhou, and R. Zhang, "“Attribute reduction based on information quantity under incomplete information systems”, Syst. Eng.-", Theory Pract., vol. 2005, no. 4, pp. 55-60, 2005.
[21]
W. Zhang, G. Wang, Z. Zhu, and J. Xiao, "Population size selection of particle swarm optimizer algorithm", Comput. Syst. Appl., vol. 19, no. 5, pp. 125-128, 2010.
[22]
G. Li, J. Zhang, F. Wen, and Y. Song, "Evaluation of real-time health condition for wind turbines considering information uncertainty", Autom. Electr. Power Syst., vol. 41, no. 18, pp. 111-117, 2017.

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