A New Transformer Winding RLC Model to Study the Effect of the Disk Space Variation on FRA Signature

Author(s):  
Venera Nurmanova ◽  
Yerbol Akhmetov ◽  
Mehdi Bagheri ◽  
Amin Zollanvari ◽  
Gevork B. Gharehpetian ◽  
...  
2016 ◽  
Vol 136 (7) ◽  
pp. 654-662
Author(s):  
Satoru Miyazaki ◽  
Yoshinobu Mizutani ◽  
Akira Taguchi ◽  
Junichi Murakami ◽  
Naokazu Tsuji ◽  
...  

Author(s):  
Zhenhua Li ◽  
Weihui Jiang ◽  
Li Qiu ◽  
Zhenxing Li ◽  
Yanchun Xu

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.


Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4242
Author(s):  
Fausto Valencia ◽  
Hugo Arcos ◽  
Franklin Quilumba

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.


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

Sign in / Sign up

Export Citation Format

Share Document