Results 1 to 10 of about 2,144 (161)

Convolutional Neural Network-Based Transformer Fault Diagnosis Using Vibration Signals [PDF]

Sensors, 2023
Fast and accurate fault diagnosis is crucial to transformer safety and cost-effectiveness. Recently, vibration analysis for transformer fault diagnosis is attracting increasing attention due to its ease of implementation and low cost, while the complex ...
Chao Li, Jie Chen, Cheng Yang, Jingjian Yang, Zhigang Liu, Pooya Davari   +5 more
doaj   +4 more sources

Transformer fault diagnosis method based on TLR-ADASYN balanced dataset [PDF]

Scientific Reports, 2023
As the cornerstone of transmission and distribution equipment, power transformer plays a very important role in ensuring the safe operation of power system.
Shan Guan, Haiqi Yang, Tongyu Wu
doaj   +2 more sources

Power transformer fault diagnosis method based on multi source signal fusion and fast spectral correlation [PDF]

Scientific Reports
Addressing the issues that signal measured by a single sensor can not provide a complete description of power transformer fault states and the problems that selection of signal features relies on manual experience, a method based on multi source signal ...
Shan Guan, Mingyu Shi, Fuwang Wang, Jinnuo Li   +3 more
doaj   +2 more sources

Transformer fault diagnosis based on adversarial generative networks and deep stacked autoencoder [PDF]

Heliyon
Establishing a deep learning model for transformer fault diagnosis using transformer oil chromatogram data requires a large number of fault samples. The lack and imbalance of oil chromatogram data can lead to overfitting, lack of representativeness of ...
Lei Zhang, Zhongyang Xu, Chen Lu, Tianjiao Qiao, Hongzhi Su, Yazhou Luo   +5 more
doaj   +2 more sources

Transformer fault diagnosis using continuous sparse autoencoder. [PDF]

Springerplus, 2016
This paper proposes a novel continuous sparse autoencoder (CSAE) which can be used in unsupervised feature learning. The CSAE adds Gaussian stochastic unit into activation function to extract features of nonlinear data. In this paper, CSAE is applied to solve the problem of transformer fault recognition. Firstly, based on dissolved gas analysis method,
Wang L, Zhao X, Pei J, Tang G.
europepmc   +3 more sources

Advancement in transformer fault diagnosis technology

Frontiers in Energy Research
The transformer plays a critical role in maintaining the stability and smooth operation of the entire power system, particularly in power transmission and distribution. The paper begins by providing an overview of traditional fault diagnosis methods for transformers, including dissolved gas analysis and vibration analysis techniques, elucidating their ...
Haiou Cao, Chenbin Zhou, Yihua Meng, Jiaoxiao Shen, Xiayin Xie   +4 more
exaly   +3 more sources

Transformer Fault Diagnosis Based on Hybrid Sampling and Support Vector Machines

Zhongguo dianli, 2021
Aiming at the impact of transformer imbalanced data set on transformer fault diagnosis model. A transformer fault diagnosis method based on hybrid sampling and support vector machines (SVM) is proposed.
Liang LI, Jin FAN, Lin YAN, Mi ZHANG, Pengfei WANG, Xiaojun ZHAO, Haibin XIAO   +6 more
doaj   +1 more source

A Siamese Vision Transformer for Bearings Fault Diagnosis

Micromachines, 2022
Fault diagnosis methods based on deep learning have progressed greatly in recent years. However, the limited training data and complex work conditions still restrict the application of these intelligent methods. This paper proposes an intelligent bearing fault diagnosis method, i.e., Siamese Vision Transformer, suiting limited training data and complex
Qiuchen He, Shaobo Li, Qiang Bai, Ansi Zhang, Jing Yang, Mingming Shen   +5 more
openaire   +3 more sources

Fault Diagnosis of Power Transformers With Membership Degree [PDF]

IEEE Access, 2019
Power transformers are important equipment for power systems, and a dissolved gas analysis (DGA) is widely used to detect incipient faults in oil-pregnant transformers. The conventional methods are prone to misinterpreting the gas data near the boundaries and the correct rate is low.
Enwen Li, Linong Wang, Bin Song
openaire   +2 more sources

CHPOA-DBN Transformer Fault Diagnosis Method Considering Sample Within-Class Imbalance

Zhongguo dianli, 2023
In recent years, deep belief network (DBN) based transformer fault diagnosis methods have been developed. However, they share two prominent drawbacks, which are the low accuracy issue caused by the within-class imbalance of transformer faults samples and
Shuang WANG, Qian LUO, Bo TANG, Lan JIANG, Jin LI   +4 more
doaj   +1 more source