Title :
Diagnosis of transformer winding deformation on the basis of artificial neural network
Author :
Zhijian, Jin ; Jingtao, Li ; Zishu, Zhu
Author_Institution :
Shanghai Jiaotong Univ., China
Abstract :
It needs much experience to determine the deformation of the transformer winding with the FRA method. This paper introduces the BP neural network and discusses some calculation improvements in order to apply it to the practical engineering problem. As a result it can recognize the deformation of the transformer winding automatically and effectively through studying various practical examples
Keywords :
automatic testing; backpropagation; feedforward neural nets; power engineering computing; power transformer testing; transformer windings; BP neural network; FRA method; artificial neural network; backpropagation; diagnosis; feedforward neural network; transformer winding deformation; Artificial neural networks; Backpropagation algorithms; Feedforward neural networks; Frequency; Neural networks; Neurons; Power transformers; Stress; US Department of Transportation; Windings;
Conference_Titel :
Properties and Applications of Dielectric Materials, 2000. Proceedings of the 6th International Conference on
Conference_Location :
Xi´an
Print_ISBN :
0-7803-5459-1
DOI :
10.1109/ICPADM.2000.875658
