Title :
Fault diagnosis in hydraulic turbine governor based on BP neural network
Author :
Xiaohui, Yu ; Ruijin, Liao ; Chenguo, Yao
Author_Institution :
Ge Zhou Ba Hydroelectric Power Station, YiChang, China
Abstract :
This paper describes a new fault diagnosis model of the hydraulic turbine governing system with the advanced BPNN (backpropagation neural network), which consists of three layers: i.e. input layer (17 neurons), hidden layer, output layer (13 neurons). It is proved that the system can rind the faults correctly in GeZhouBa hydroelectric power station, and it can conduct the faults examination and repair of governing systems. So this diagnosis system should be applied widely in practice
Keywords :
backpropagation; diagnostic expert systems; fault diagnosis; hydraulic turbines; hydroelectric power stations; machine testing; maintenance engineering; neural nets; turbogenerators; China; Gezhouba hydroelectric power station; backpropagation neural network; fault diagnosis model; hidden layer; hvdraulic turbine governing system; input laver; output layer; Artificial intelligence; Fault diagnosis; Hydraulic turbines; Intelligent networks; Neural networks; Neurons; Power generation; Power system faults; Power system modeling; Power system reliability;
Conference_Titel :
Electrical Machines and Systems, 2001. ICEMS 2001. Proceedings of the Fifth International Conference on
Conference_Location :
Shenyang
Print_ISBN :
7-5062-5115-9
DOI :
10.1109/ICEMS.2001.970680
