Research on imbalanced data fault diagnosis of on-load tap changers based on IGWO-WELM

Yan Yan; Yong Qian; Hongzhong Ma; Changwu Hu; Yan Yan; Yong Qian; Hongzhong Ma; Changwu Hu

doi:10.3934/mbe.2023226

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 3: 4877-4895. doi: 10.3934/mbe.2023226

Previous Article Next Article

Research article

Research on imbalanced data fault diagnosis of on-load tap changers based on IGWO-WELM

1.
State Grid Ningxia Electric Power Research Institute, Yinchuan 750002, China
2.
College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
3.
Zhongwei Power Supply Company of State Grid Ningxia Electric Power Co. Ltd, Zhongwei 755000, China

Academic Editor: Yang Kuang

Received: 21 October 2022 Revised: 11 December 2022 Accepted: 16 December 2022 Published: 04 January 2023

Aiming at the problem of on-load tap changer (OLTC) fault diagnosis under imbalanced data conditions (the number of fault states is far less than that of normal data), this paper proposes an OLTC fault diagnosis method based on an Improved Grey Wolf algorithm (IGWO) and Weighted Extreme Learning Machine (WELM) optimization. Firstly, the proposed method assigns different weights to each sample ac-cording to WELM, and measures the classification ability of WELM based on G-mean, so as to realize the modeling of imbalanced data. Secondly, the method uses IGWO to optimize the input weight and hidden layer offset of WELM, avoiding the problems of low search speed and local optimization, and achieving high search efficiency. The results show that IGWO-WLEM can effectively diagnose OLTC faults under imbalanced data conditions, with an improvement of at least 5% compared with existing methods.
- imbalanced data,
- OLTC,
- IGWO,
- WELM,
- IGWO-WELM
Citation: Yan Yan, Yong Qian, Hongzhong Ma, Changwu Hu. Research on imbalanced data fault diagnosis of on-load tap changers based on IGWO-WELM[J]. Mathematical Biosciences and Engineering, 2023, 20(3): 4877-4895. doi: 10.3934/mbe.2023226

Related Papers:

Abstract

Aiming at the problem of on-load tap changer (OLTC) fault diagnosis under imbalanced data conditions (the number of fault states is far less than that of normal data), this paper proposes an OLTC fault diagnosis method based on an Improved Grey Wolf algorithm (IGWO) and Weighted Extreme Learning Machine (WELM) optimization. Firstly, the proposed method assigns different weights to each sample ac-cording to WELM, and measures the classification ability of WELM based on G-mean, so as to realize the modeling of imbalanced data. Secondly, the method uses IGWO to optimize the input weight and hidden layer offset of WELM, avoiding the problems of low search speed and local optimization, and achieving high search efficiency. The results show that IGWO-WLEM can effectively diagnose OLTC faults under imbalanced data conditions, with an improvement of at least 5% compared with existing methods.

References

[1]	Y. Yan, H. Ma, M. Wen, S. Dang, H. Xu, Multi-feature fusion-based mechanical fault diagnosis for on-load tap changers in smart grid with electric vehicles, IEEE Sens. J., 21 (2021), 15696-15708. https://doi.org/10.1109/JSEN.2020.3021920 doi: 10.1109/JSEN.2020.3021920
[2]	K. Wongon, K. Sunuwe, J. Jingyo, H. Kim, H. Lee, B. D. Youn, Digital twin approach for on-load tap changers using data-driven dynamic model updating and optimization-based operating condition estimation, Mech. Syst. Signal PR., 181 (2022), 1-17. https://doi.org/10.1016/j.ymssp.2022.109471 doi: 10.1016/j.ymssp.2022.109471
[3]	Q. Yang, J. Ruan, Z. Zhuang, Fault diagnosis of circuit breakers based on time–frequency and chaotic vibration analysis, IET Gener. Transm. Dis., 14 (2020), 1214–1221. https://doi.org/10.1049/iet-gtd.2019.0985 doi: 10.1049/iet-gtd.2019.0985
[4]	R. Y. Shang, C. Q. Peng, P. F. Shao, R. Fang, FFT-based equal-integral-bandwidth feature extraction of vibration signal of OLTC, Math. Biosci. Eng., 18 (2021), 1996–1980. https://doi.org/10.3934/mbe.2021102 doi: 10.3934/mbe.2021102
[5]	C. Bengtsson, Status and trends in transformer monitoring, IEEE Trans. Power deliver., 11 (1996), 1379–1384. https://doi.org/10.1109/61.517495 doi: 10.1109/61.517495
[6]	P. Kang, D. Birtwhistle.Condition monitoring of power transformer on-load tap-changers.Part I: Automatic condition diagnostics, IEE P-Gener. Transm. D., 148 (2001), 301–306. https://doi.org/10.1049/ip-gtd:20010389 doi: 10.1049/ip-gtd:20010389
[7]	P. Kang, D. Birtwhistle.Condition monitoring of power transformer on-load tap-changers.Part Ⅱ:Detection of ageing from vibration signatures, IEE P-Gener. Transm. D., 148 (2001), 307–311. https://doi.org/10.1049/ip-gtd:20010388 doi: 10.1049/ip-gtd:20010388
[8]	P. Kang, D. Birtwhistle. Condition assessment of power transformer on-load tap changers using wavelet analysis and self-organizing map: field evaluation, IEEE Trans. Power deliver., 18 (2003), 78–84. https://doi.org/10.1109/TPWRD.2002.803692 doi: 10.1109/TPWRD.2002.803692
[9]	X. Zhou, F. H. Wang, J. Fu, J. Lin, Mechanical condition monitoring of on-load tap changers based on chaos theory and k-means clustering method, Proc. CSEE, 35 (2015), 1541–1548. https://doi.org/10.13334/j.0258-8013.pcsee.2015.06.031 doi: 10.13334/j.0258-8013.pcsee.2015.06.031
[10]	J. X. Liu, G. Wang, T. Zhao, L. Zhang, Fault diagnosis of on-load tap-changer based on variational mode decomposition and relevance vector machine, Energies, 10 (2017), 946–959. https://doi.org/10.3390/en10070946 doi: 10.3390/en10070946
[11]	X. Duan, T. Zhao, T. Li, J. Liu, L. Zou, L. Zhang, Method for diagnosis of on-load tap-changer based on wavelet theory and support vector machine, J. Eng. Ny., 13 (2017), 2193–2197. https://doi.org/10.1049/joe.2017.0719 doi: 10.1049/joe.2017.0719
[12]	Q. Li, T. Zhao, Z. Li, J. Lou, Mechanical fault diagnosis of on load tap changer within power transformers based on hidden Markov model, IEEE Trans. Power deliver., 27 (2012), 596–601. https://doi.org/10.1109/TPWRD.2011.2175454 doi: 10.1109/TPWRD.2011.2175454
[13]	X. Liang, Y. Wang, H. Gu, A mechanical fault diagnosis model of on-load tap changer based on same-source heterogeneous data fusion, IEEE Trans. Instrum. Meas., 71 (2022). https://doi.org/10.1109/TIM.2021.3064808
[14]	L. Zheng, G. Liu, C. Yan, C. Jiang, et al. Improved TradaBoost and its application to transaction fraud detection, IEEE Trans. Comput. Social Syst., 7 (2020), 1304–1316. https://doi.org/10.1109/TCSS.2020.3017013 doi: 10.1109/TCSS.2020.3017013
[15]	S. Dhote., C. Vichoray, R. Pais, et al., Hybrid geometric sampling and AdaBoost based deep learning approach for data imbalance in E-commerce, Electron. Commer. Res., 20 (2020), 259–274. https://doi.org/10.1007/s10660-019-09383-2 doi: 10.1007/s10660-019-09383-2
[16]	W. Lee, C. H. Jun, J. Lee, Instance categorization by support vector machines to adjust weights in AdaBoost for imbalanced data classification, Inform. Sci., 381 (2017), 92–103. https://doi.org/10.1016/j.ins.2016.11.014 doi: 10.1016/j.ins.2016.11.014
[17]	Y. Gao, L. Gao, X. Li, S. Cao, A hierarchical training-convolutional neural network for imbalanced fault diagnosis in complex equipment, IEEE Trans. Ind. Inform., 18 (2022), 8138–8145. https://doi.org/10.1109/TⅡ.2022.3177662 doi: 10.1109/TⅡ.2022.3177662
[18]	Y. Geng, X. Y. Luo, Cost-sensitive convolutional neural networks for imbalanced time series classification, Intell. Data Anal., 23 (2019), 357–370. https://doi.org/10.3233/IDA-183831 doi: 10.3233/IDA-183831
[19]	A. Taherhnai, G. Cosma, T. T. McGinnity, AdaBoost-CNN: An adaptive boosting algorithm for convolutional neural networks to classify multi-class imbalanced datasets using transfer learning, Neurocomputing, 404 (2020), 351–366. https://doi.org/10.1016/j.neucom.2020.03.064 doi: 10.1016/j.neucom.2020.03.064
[20]	Y. Xie, G. Liu, C. Yan, C. Jiang, M. Zhou, M. Li, Learning transactional behavioral representations for credit card fraud detection, IEEE Trans. Neural Networks Learn. Syst., 2022. https://doi.org/10.1109/TNNLS.2022.3208967 doi: 10.1109/TNNLS.2022.3208967
[21]	Q. X. Zhu, X. W. Wang, N. Zhang, Novel K-Medoids based SMOTE integrated with locality preserving projections for fault diagnosis, IEEE Trans. Instrum. Meas., 71 (2022). https://doi.org/10.1109/TIM.2022.3218551
[22]	Y. Gao, Q. C. Liu, An over sampling method of unbalanced data based on ant colony clustering, IEEE Access, 9 (2021), 130990–130996. https://doi.org/10.1109/ACCESS.2021.3114443 doi: 10.1109/ACCESS.2021.3114443
[23]	J. Shen, J.C. Wu, M. Xu, D. Gan, B. An, F. Liu, A hybrid method to predict postoperative survival of lung cancer using improved SMOTE and adaptive SVM, Comput. Math. Method Med., 2021 (2021). https://doi.org/10.1155/2021/2213194 doi: 10.1155/2021/2213194
[24]	K. Hu, Z. Zhou, L. Weng, J. Liu, L. Wang, Y. Su, et al., An optimization strategy for weighted extreme learning machine based on PSO, Int. J. Pattern. Recogn., 31 (2017). https://doi.org/10.1142/S0218001417510016
[25]	Z. Z. Li, M. Huang, G. J. G. Liu, Optimizing weighted extreme learning machines for imbalanced classification and application to credit card fraud detection, Expert Syst. Appl., 175 (2021). https://doi.org/10.1016/j.eswa.2021.114750
[26]	S. Saremi, S. Z. Mirjalili, S. M. Mirjalili. Evolutionary population dynamics and grey Wolf optimizer, Neural Comput. Appl., 26 (2015), 1257–1253. https://doi.org/10.1007/s00521-014-1806-7 doi: 10.1007/s00521-014-1806-7
[27]	G. M. Komaki, V. Kayvanfar. Grey Wolf optimizer for the two-stage assembly flow shop scheduling problems with release time, J. Comput. Sci., 8 (2015), 109–120. https://doi.org/10.1016/j.jocs.2015.03.011 doi: 10.1016/j.jocs.2015.03.011
[28]	A. K. Mishra, S. R. Das, P. K. Ray, R. K. Mallick, A. Mohanty, D. K. Mishra, PSO-GWO optimized fractional order PID based hybrid shunt active power filter for power quality improvements, IEEE Access, 8 (2020), 74497–74512. https://doi.org/10.1109/ACCESS.2020.2988611 doi: 10.1109/ACCESS.2020.2988611
[29]	H. H. Zhu, G. J. Liu, M. C., Zhou, Y. Xie, Q. Kang, Dandelion algorithm with probability-based mutation, IEEE Access, 7 (2019), 97974–97985. https://doi.org/10.1109/ACCESS.2019.2927846 doi: 10.1109/ACCESS.2019.2927846
[30]	A. K. Dutta, B. Qureshi, Y. Albagory, Ma. Alsanea, M. Al Faraj, A. R. W. Sait, Optimal weighted extreme learning machine for cybersecurity fake news classification, Comput. Syst. Sci. Eng., 44 (2023), 2395–2409. https://doi.org/10.32604/csse.2023.027502 doi: 10.32604/csse.2023.027502
[31]	G. B. Allende, G. Still, Solving bilevel programs with the KKT-approach, Math. Program., 138 (2013), 309–332. https://doi.org/10.1007/s10107-012-0535-x doi: 10.1007/s10107-012-0535-x
[32]	C. Lei, S. Wan, Intelligent fault diagnosis of high-voltage circuit breakers using triangular global alignment kernel extreme learning machine, ISA Trans., 109 (2020), 368–379. https://doi.org/10.1016/j.isatra.2020.10.018 doi: 10.1016/j.isatra.2020.10.018
[33]	Z. C. Li, M. Huang, G. J. Liu, C. Jiang, A hybrid method with dynamic weighted entropy for handling the problem of class imbalance with overlap in credit card fraud detection, Expert Syst. Appl., 175 (2021), 114750. https://doi.org/10.1016/j.eswa.2021.114750 doi: 10.1016/j.eswa.2021.114750
[34]	C. B. Liu, H. F. Ke, G. Zhang, Y. Mei, H. Xu, An improved weighted extremely learning machine for imbalanced data classification, Memetic Comput., 20 (2019), 27–34. https://doi.org/10.1007/s12293-017-0236-3 doi: 10.1007/s12293-017-0236-3
[35]	L. Chen, S. Wan, L. Dou, Improving diagnostic performance of High-Voltage circuit breakers on imbalanced data using an oversampling method, IEEE Trans. Power deliver., 37 (2022), 2704–2716. https://doi.org/10.1109/TPWRD.2021.3114547 doi: 10.1109/TPWRD.2021.3114547

Reader Comments

Your name:*

Email:*
© 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)