Results 21 to 30 of about 2,243 (260)

Research on mechanical fault diagnosis method of power transformer winding

The Journal of Engineering, 2018
Today, the accuracy of the fault mechanical diagnosis of transformer winding is low and the fault types cannot be judged, this study proposes a machine condition diagnosis method of transformer winding based on the combination of short-circuit reactance ...
Zhang Bin, Zhao Dan, Wang Feiming, Shi Kejian, Zhao Zhenyang   +4 more
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A Novel Extension Method for Transformer Fault Diagnosis [PDF]

IEEE Power Engineering Review, 2002
Dissolved gas analysis (DGA) is one of the most useful techniques to detect incipient faults in power transformers. However, the identification of the faulted location by the traditional method is not always an easy task due to the variability of gas data and operational variables.
openaire   +1 more source

Research of Transformer Intelligent Evaluation and Diagnosis Method Based on DGA

MATEC Web of Conferences, 2016
Oil chromatography analysis is the foundation of transformer fault diagnosis, In this paper, based on knowledge base of the oil chromatographic analysis, an extended research combining the improved three ratio method, BP neural network, and case-based ...
Wang Feng, Bi Jiangang, Zhang Bowen, Yuan Shuai   +3 more
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Gaussian-Linearized Transformer with Tranquilized Time-Series Decomposition Methods for Fault Diagnosis and Forecasting of Methane Gas Sensor Arrays

Applied Sciences, 2023
Methane is considered as a clean energy that is widely used in places with high environmental requirements. The increasing demand for methane exploration in polar and deep sea extreme environments has a positive role in carbon neutrality policies.
Kai Zhang, Wangze Ning, Yudi Zhu, Zhuoheng Li, Tao Wang, Wenkai Jiang, Min Zeng, Zhi Yang   +7 more
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Fuzzy Ontology Reasoning for Power Transformer Fault Diagnosis

Advances in Electrical and Computer Engineering, 2015
This paper presents a novel fuzzy ontology reasoner for power transformer fault diagnosis under a multi-agent framework. The developed ontology provides a comprehensive knowledge base as part of a multi-agent system to enable imprecision reasoning.
SAMIRMI, F. D., TANG, W., WU, Q.
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Improved Genetic Algorithm and XGBoost Classifier for Power Transformer Fault Diagnosis

Frontiers in Energy Research, 2021
Power transformer is an essential component for the stable and reliable operation of electrical power grid. The traditional transformer fault diagnostic methods based on dissolved gas analysis are limited due to the low accuracy of fault identification ...
Zhanhong Wu, Mingbiao Zhou, Zhenheng Lin, Xuejun Chen, Yonghua Huang   +4 more
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Fault diagnosis of mind-used transformer based on stacked sparse auto-encoder

Gong-kuang zidonghua, 2018
In view of application of deep learning to transformer fault diagnosis had a good fault diagnosis effect, a fault diagnosis method of mind-used transformer based on stacked sparse auto-encoder was proposed.
XU Qianwen, JI Xingquan, ZHANG Yuzhen, LI Jun, YU Yongjin   +4 more
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Development of system of on-line monitoring, fault diagnosis and prewarning for insulation of mine-used dry-type transformer

Gong-kuang zidonghua, 2013
In view of problems that monitoring variable is single and real-time status of insulation of mine-used dry-type transformer cannot be reflected existed in traditional monitoring method of mine-used dry-type transformer because it uses thermocouple to ...
WAN Zhi-qiang, SONG Jian-cheng, LEI Zhi-peng, WEN Min-min, LI Chuan-yang   +4 more
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Fault Diagnosis of Power Transformer Based on Time-Shift Multiscale Bubble Entropy and Stochastic Configuration Network

Entropy, 2022
In order to accurately diagnose the fault type of power transformer, this paper proposes a transformer fault diagnosis method based on the combination of time-shift multiscale bubble entropy (TSMBE) and stochastic configuration network (SCN).
Fei Chen, Wanfu Tian, Liyao Zhang, Jiazheng Li, Chen Ding, Diyi Chen, Weiyu Wang, Fengjiao Wu, Bin Wang   +8 more
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A Method of Transformer Fault Diagnosis Based onImproved BP Neural Network

Kongzhi Yu Xinxi Jishu, 2021
Relationship between transformer internal faults and gases dissolved in oil with different components and different contents is the main research content of transformer fault diagnosis.
GUO Lin, TANG Jing, TANG Lizhe, ZHAN Yanhao, LI Fei   +4 more
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