Load influence on lissajous figure for online transformer winding diagnosis

2016 ◽  
Author(s):  
Yu X. Liao ◽  
Tian Y. Zhu ◽  
Yi Q. Sun ◽  
Jie Zhang ◽  
Tao Cheng ◽  
...  
Keyword(s):  
Lissajous Figure ◽  
Transformer Winding

Diagnosis Criterion of Abnormality of Transformer Winding by Frequency Response Analysis (FRA)

2016 ◽  
Vol 136 (7) ◽  
pp. 654-662
Author(s):  
Satoru Miyazaki ◽  
Yoshinobu Mizutani ◽  
Akira Taguchi ◽  
Junichi Murakami ◽  
Naokazu Tsuji ◽  
...  
Keyword(s):  
Frequency Response ◽  
Response Analysis ◽  
Frequency Response Analysis ◽  
Transformer Winding ◽  
Diagnosis Criterion

Research Situation Analysis of On-line Detection Technology for Power Transformer Winding Deformation

2020 ◽  
Vol 13 (6) ◽  
pp. 823-832
Author(s):  
Zhenhua Li ◽  
Weihui Jiang ◽  
Li Qiu ◽  
Zhenxing Li ◽  
Yanchun Xu
Keyword(s):  
Detection Method ◽  
Theoretical Research ◽  
Line Detection ◽  
Response Analysis ◽  
Advantages And Disadvantages ◽  
Detection Technology ◽  
The Future ◽  
Transformer Winding ◽  
On Line ◽  
Winding Deformation

Background: Winding deformation is one of the most common faults in power transformers, which seriously threatens the safe operation of transformers. In order to discover the hidden trouble of transformer in time, it is of great significance to actively carry out the research of transformer winding deformation detection technology. Methods: In this paper, several methods of winding deformation detection with on-line detection prospects are summarized. The principles and characteristics of each method are analyzed, and the advantages and disadvantages of each method as well as the future research directions are expounded. Finally, aiming at the existing problems, the development direction of detection method for winding deformation in the future is prospected. Results: The on-line frequency response analysis method is still immature, and the vibration detection method is still in the theoretical research stage. Conclusion: The ΔV − I1 locus method provides a new direction for on-line detection of transformer winding deformation faults, which has certain application prospects and practical engineering value.

scholarly journals Prediction of Stress in Power Transformer Winding Conductors Using Artificial Neural Networks: Hyperparameter Analysis

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4242
Author(s):  
Fausto Valencia ◽  
Hugo Arcos ◽  
Franklin Quilumba
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Power Transformer ◽  
Network Models ◽  
Activation Function ◽  
Neural Network Models ◽  
Transformer Winding ◽  
Artificial Neural ◽  
Femm Software

The purpose of this research is the evaluation of artificial neural network models in the prediction of stresses in a 400 MVA power transformer winding conductor caused by the circulation of fault currents. The models were compared considering the training, validation, and test data errors’ behavior. Different combinations of hyperparameters were analyzed based on the variation of architectures, optimizers, and activation functions. The data for the process was created from finite element simulations performed in the FEMM software. The design of the Artificial Neural Network was performed using the Keras framework. As a result, a model with one hidden layer was the best suited architecture for the problem at hand, with the optimizer Adam and the activation function ReLU. The final Artificial Neural Network model predictions were compared with the Finite Element Method results, showing good agreement but with a much shorter solution time.

Influence of moisture dependency of pressboard on transformer winding clamping pressure

2017 ◽  
Vol 24 (5) ◽  
pp. 3191-3200 ◽  
Author(s):  
Lakshitha Naranpanawe ◽  
Chandima Ekanayake ◽  
Tapan Kumar Saha ◽  
Pratheep K. Annamalai
Keyword(s):  
Transformer Winding ◽  
Clamping Pressure
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