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104 results on '"Wang, Tonglei"'

1. Fault diagnosis method for OLTC based on improved semi-supervised ladder networks

Author

ZHENG Shangzhi, ZHONG Linlin, WANG Tonglei, and GAO Bingtuan

Subjects
on-load tap changer (oltc), fault diagnosis, vibration signals, ladder networks, semi-supervised learning, bayesian optimization, Applications of electric power, TK4001-4102
Abstract

On-load tap changers (OLTC) have complex mechanical and electrical structures, which are the key component for the on-load voltage regulation of transformers. Currently, due to the sample data which are not easy to be labeled, it is difficult to effectively train the OLTC mechanical fault diagnosis models based on vibration signals. To improve the fault diagnostic accuracy for OLTC with limited labeled data, a fault diagnosis method based on Bayesian optimization-convolutional ladder networks (BO-ConvLN) is proposed in this paper. Firstly, the ladder networks are used as a semi-supervised learning method for the feature extraction of vibration signals, which is guided by a large amount of unlabeled data, leading to the enhanced diagnostic ability of ladder networks only with a small amount of labeled data. Then, the fully-connected layers are replaced by convolutional operators in the ladder networks to better extract the features of non-stationary vibration signals. Furthermore, Bayesian optimization is used to optimize the high-dimensional hyperparameters of ladder networks, witch significantly improves the diagnostic accuracy of the model within limited time cost. The experiment results show that the diagnostic accuracy for the three types of faults, namely transmission shaft jams, poor switch lubrication, and top cover looseness, is 91.67% with a label count of only 40, which demonstrates the effectiveness of BO-ConvLN in the fault diagnosis.

Published
2023
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2. The Fault Diagnosis of Infrared Bushing Images Based on Infrared Thermography

Author

Wei, Chao, Liu, Yang, Bie, Yifan, Wang, Shengquan, Wu, Yiming, Wang, Tonglei, Yin, Kangyong, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martin, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Xue, Yusheng, editor, Zheng, Yuping, editor, and Rahman, Saifur, editor

Published
2020
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7. Transformer Fault Diagnosis Method Based on Incomplete Data and TPE-XGBoost.

Author

Wang, Tonglei, Li, Qun, Yang, Jinggang, Xie, Tianxi, Wu, Peng, and Liang, Jiabi

Subjects
FAULT diagnosis, ARTIFICIAL neural networks, BOOSTING algorithms, MISSING data (Statistics), MACHINE learning, REGRESSION trees, DIAGNOSIS methods
Abstract

Dissolved gas analysis is an important method for diagnosing the operating condition of power transformers. Traditional methods such as IEC Ratios and Duval Triangles and Pentagon methods are not applicable in the case of abnormal or missing values of DGA data. A novel transformer fault diagnosis method based on an extreme gradient boosting algorithm is proposed in this paper. First, the traditional statistical method is replaced by the random forest regression algorithm for filling in missing values of dissolved gas data. Normalization and feature derivation of the outlier data is adopted based on the gas content. Then, hyperparameter optimization of the transformer fault diagnosis model based on an extreme gradient boosting algorithm is carried out using the tree-structured probability density estimator algorithm. Finally, the influence of missing data and optimization algorithms on transformer fault diagnosis models is analyzed. The effects of different algorithms based on incomplete datasets are also discussed. The results show that the performance of the random forest regression algorithm on missing data filling is better than classification and regression trees and traditional statistical methods. The average accuracy of the fault diagnosis method proposed in the paper is 89.5%, even when the missing data rate reaches 20%. The accuracy and robustness of the TPE-XGBoost model are superior to other machine learning algorithms described in this paper, such as k-nearest neighbor, deep neural networks, random forest, etc. [ABSTRACT FROM AUTHOR]

Published
2023
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19. Influence of Metal Particles on Discharge Characteristics of Insulating Oil under Lightning Impulse

Author

Jiansheng Li, Xiaoping Yang, Youyuan Wang, Chao Wei, Bonan Li, Houying Li, Yuncai Lu, Sun Lei, and Wang Tonglei

Subjects
Metal, Materials science, Transformer oil, law, Test platform, visual_art, visual_art.visual_art_medium, Breakdown voltage, Composite material, Transformer, Metal particle, Voltage, law.invention
Abstract

In this paper, a research work on the influence of metal particles on lightning impulse discharge characteristics of transformer insulating oil is carried out. The main research contents are as follows: The lightning impulse discharge test platform for insulating oil was established. The influence of the development characteristics and breakdown characteristics of the streamer in the insulating oil with different metal particle concentration under pre-breakdown voltage was studied. The discharge characteristics of pure insulating oil and particle-containing granular insulating oil were compared and analyzed. The breakdown characteristics of insulating oil with different metal particle concentration under different oil gaps were measured, and the influence of metal particles on the lightning breakdown characteristics of insulating oil was obtained. The results show that the metal particles promote the initiation and development of the streamer in the oil. The stop length and development speed of the streamer are positively correlated with the particle concentration, while the initial voltage of the insulating oil is negatively correlated with the breakdown voltage and the particle concentration.

Published
2019

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