Title :
A fault diagnosis method of the main transformer in the power train using compound data fusion method
Author :
Ze-Ming Fan ; Li Tong ; Ding Liu ; Wen-Sheng Wang
Author_Institution :
Postdoctoral Res. Station, Xi´´an Univ. of Technol., China
Abstract :
By analyzing the work principle and structure of the main transformer in power train, according to the modern data fusion theory, the paper presents a fault diagnosis method of the transformer using the compound data fusion method based on the dissolved matter-in-oil and other information. First by the BP network, the data that comes from the sensors is done with compensation and the pretreatments. Then the data is transmitted to the center data fusion unit that contains several model fusion methods and enters into the suitable fusion arithmetic to complete the fusion separately and automatically on the basis of the type of the datum, and then the outputting information comes into the expert arbitration unit to realize the precise fault diagnosis. The experiment results show that the method is feasible and efficient and of high precision to the fault diagnosis of the main transformer in power train.
Keywords :
backpropagation; compensation; fault diagnosis; power transformers; sensor fusion; backpropagation network; compensation; compound data fusion method; fault diagnosis method; power train main transformer; Arithmetic; Circuit faults; Electric variables measurement; Fault diagnosis; Gas insulation; Information analysis; Magnetic circuits; Oil insulation; Petroleum; Power transformer insulation;
Conference_Titel :
Machine Learning and Cybernetics, 2003 International Conference on
Print_ISBN :
0-7803-8131-9
DOI :
10.1109/ICMLC.2003.1259709