DocumentCode :
1731353
Title :
Rotor Winding Inter-Turn Short Circuit Fault Diaginosis System Based on Artificial Neural Network
Author :
Shuting, Wan ; Peng, He
Author_Institution :
North China Electr. Power Univ., Baoding
fYear :
2007
Abstract :
The diagnosis system of generator rotor winding inter-turn short circuit fault based on artificial neural network (ANN) is developed. First, adapting for the need of faster speed and higher diagnosis precision of fault diagnosis, a new faster back-propagation (BP) with the error contracting gradually algorithm is selected, and the diagnosis model of rotor winding inter-turn short circuit fault based on ANN is proposed. Then the diagnosis system is developed by Visual Basic and SQL Server database, which uses the RS485 digit communication port of the exciting current meter and power meter of the generator to collect the data of exciting current, active power, inactive power, and uses the NPort to transfer them to the SQL Server database of the server. Finally, the diagnosis system is successfully applied to power plant.
Keywords :
backpropagation; data acquisition; fault diagnosis; machine testing; machine windings; neural nets; power engineering computing; rotors; short-circuit currents; NPort; RS485 digit communication port; SQL Server database; Visual Basic; artificial neural network; back-propagation; current meter; data acquisition; error contracting gradually algorithm; exciting current; generator rotor winding; inactive power; inter-turn short circuit fault diagnosis system; power meter; power plant; Artificial neural networks; Circuit faults; Fault diagnosis; Instruments; Neural networks; Neurons; Power generation; Power measurement; Rotors; Visual databases; Generator; artificial neural network(ANN); data acquisition; fault diagnosis system; rotor winding inter-turn short circuit fault;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electronic Measurement and Instruments, 2007. ICEMI '07. 8th International Conference on
Conference_Location :
Xi´an
Print_ISBN :
978-1-4244-1136-8
Electronic_ISBN :
978-1-4244-1136-8
Type :
conf
DOI :
10.1109/ICEMI.2007.4350984
Filename :
4350984
Link To Document :
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