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

Hu, Huan; Xu, Kang; Zhang, Xianya; Li, Fangjing; Zhu, Lingling; Xu, Rui; Li, Deng

doi:10.3390/electronics14030625

Open AccessArticle

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

by

Huan Hu

¹,

Kang Xu

¹,

Xianya Zhang

¹,

Fangjing Li

¹,

Lingling Zhu

¹,

Rui Xu

^2,*

and

Deng Li

³

¹

Xiaogan Power Supply Company, State Grid Hubei Electric Power Co., Ltd., Xiaogan 432000, China

²

School of Computer Science and Engineering, Central South University, Changsha 410083, China

³

School of Electronics Information, Central South University, Changsha 410083, China

^*

Author to whom correspondence should be addressed.

Electronics 2025, 14(3), 625; https://doi.org/10.3390/electronics14030625

Submission received: 31 December 2024 / Revised: 17 January 2025 / Accepted: 28 January 2025 / Published: 5 February 2025

Download

Browse Figures

Versions Notes

Abstract

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%.

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

Share and Cite

MDPI and ACS Style

Hu, H.; Xu, K.; Zhang, X.; Li, F.; Zhu, L.; Xu, R.; Li, D. Research on Predictive Maintenance Methods for Current Transformers with Iron Core Structures. Electronics 2025, 14, 625. https://doi.org/10.3390/electronics14030625

AMA Style

Hu H, Xu K, Zhang X, Li F, Zhu L, Xu R, Li D. Research on Predictive Maintenance Methods for Current Transformers with Iron Core Structures. Electronics. 2025; 14(3):625. https://doi.org/10.3390/electronics14030625

Chicago/Turabian Style

Hu, Huan, Kang Xu, Xianya Zhang, Fangjing Li, Lingling Zhu, Rui Xu, and Deng Li. 2025. "Research on Predictive Maintenance Methods for Current Transformers with Iron Core Structures" Electronics 14, no. 3: 625. https://doi.org/10.3390/electronics14030625

APA Style

Hu, H., Xu, K., Zhang, X., Li, F., Zhu, L., Xu, R., & Li, D. (2025). Research on Predictive Maintenance Methods for Current Transformers with Iron Core Structures. Electronics, 14(3), 625. https://doi.org/10.3390/electronics14030625

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Menu

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

Abstract

Share and Cite

Article Metrics

Article Access Statistics

Further Information

Guidelines

MDPI Initiatives

Follow MDPI