Title :
Study on fault diagnosis of power transformer based on RSNN
Author_Institution :
Dept. of Inf. Eng., Tangshan Coll., Tangshan, China
Abstract :
This paper studies the power transformer fault quality diagnosis using the technology of intelligent diagnosis. It is rough set theory as the pre-unit of neural network. A large number of original data is made reduction by means of rough set algorithm and become the train data of BP neural network. Through simulation with practical data it is proved that the method of RSNN can make the training time shorter and the diagnostic accuracy is higher compared with the traditional neural network method.
Keywords :
backpropagation; fault diagnosis; neural nets; power engineering computing; power transformers; rough set theory; BP neural network; RSNN; fault diagnosis; intelligent diagnosis; power transformer fault quality diagnosis; rough set theory; Accuracy; Discharges (electric); Fault diagnosis; Neural networks; Power transformers; Set theory; Training; fault diagnosis; neural network; power transformer; rough set;
Conference_Titel :
Mechatronic Sciences, Electric Engineering and Computer (MEC), Proceedings 2013 International Conference on
Conference_Location :
Shengyang
Print_ISBN :
978-1-4799-2564-3
DOI :
10.1109/MEC.2013.6885095
