Research on Predictive Maintenance Methods for Current Transformers with Iron Core Structures

被引：0

作者：

Hu, Huan ^{[1
]}

Xu, Kang ^{[1
]}

Zhang, Xianya ^{[1
]}

Li, Fangjing ^{[1
]}

Zhu, Lingling ^{[1
]}

Xu, Rui ^{[2
]}

Li, Deng ^{[3
]}

机构：

[1] State Grid Hubei Elect Power Co Ltd, Xiaogan Power Supply Co, Xiaogan 432000, Peoples R China

[2] Cent South Univ, Sch Comp Sci & Engn, Changsha 410083, Peoples R China

[3] Cent South Univ, Sch Elect Informat, Changsha 410083, Peoples R China

来源：

ELECTRONICS | 2025年 / 14卷 / 03期

关键词：

current transformer; predictive maintenance; wavelet transform; feature selection; random forest; fault prediction;

D O I：

10.3390/electronics14030625

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The reliable operation of power systems is heavily dependent on effective maintenance strategies for critical equipment. Current maintenance methods are typically categorized into corrective, preventive, and predictive approaches. While corrective maintenance often results in significant downtime and preventive maintenance can be inefficient, predictive maintenance emerges as a promising technique for accurately forecasting faults. In this study, we investigated the diagnosis and prediction of fault states, specifically single-phase short circuit (1HCF) and double-phase short circuit (2HCF) faults, using monitoring data from current transformers in 110 kV substations. We proposed a predictive maintenance method for current transformers based on core-type structures, which integrates wavelet transform to extract multi-level frequency domain features, employs feature selection techniques (including the Spearman correlation coefficient and mutual information) to identify key predictive features, and utilizes Random Forest classifiers for fault state prediction. Experimental results demonstrate an overall prediction accuracy of 94%.

引用

页数：17

共 22 条

[1] Moleda M., Malysiak-Mrozek B., Ding W., Sunderam V., Mrozek D., From corrective to predictive maintenance—A review of maintenance approaches for the power industry, Sensors, 23, (2023)
[2] Mao K., Wu F., Xue H., Innovation in Data-Based Predictive Maintenance Methods, Equip. Manag. Maint, pp. 32-34, (2023)
[3] Zonta T., Da Costa C.A., da Rosa R.R., de Lima M.J., da Trindade E.S., Li G.P., Predictive maintenance in the Industry 4.0: A systematic literature review, Comput. Ind. Eng, 150, (2020)
[4] Zhang W., Yang D., Wang H., Data-driven methods for predictive maintenance of industrial equipment: A survey, IEEE Syst. J, 13, pp. 2213-2227, (2019)
[5] Divya D., Marath B., Santosh Kumar M.B., Review of fault detection techniques for predictive maintenance, J. Qual. Maint. Eng, 29, pp. 420-441, (2023)
[6] Velasquez R.M.A., Lara J.V.M., Current transformer failure caused by electric field associated to circuit breaker and pollution in 500 kV substations, Eng. Fail. Anal, 92, pp. 163-181, (2018)
[7] Wu Z., Huang T., Wang C., Wu X., Tu Y., Transformer Fault Diagnosis and Location Method Based on Fault Tree Analysis, Scalable Comput. Pract. Exp, 25, pp. 3587-3593, (2024)
[8] Gupta A., Demiroglu M., Isaacs N., Analysis of Offshore Platform Current Transformer Failures, Proceedings of the 2022 IEEE IAS Petroleum and Chemical Industry Technical Conference (PCIC), pp. 245-252
[9] Khalyasmaa A.I., Senyuk M.D., Eroshenko S.A., Analysis of the state of high-voltage current transformers based on gradient boosting on decision trees, IEEE Trans. Power Deliv, 36, pp. 2154-2163, (2020)
[10] Alhamd Q., Saniei M., Seifossadat S.G., Mashhour E., Advanced Fault Detection in Power Transformers Using Improved Wavelet Analysis and LSTM Networks Considering Current Transformer Saturation and Uncertainties, Algorithms, 17, (2024)

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