Title :
An Intelligent Fault Diagnostics for Turbine Generator by Modified Neural Model
Author :
Weng, Pin-Hsuan ; Yang, Jen-Pin ; Liu, Fang-Tsung ; Huang, Huang-Chu ; Yu, Ker-Wei ; Hwang, Rey-Chue
Author_Institution :
Electr. Eng. Dept., I-Shou Univ., Kaohsiung
Abstract :
In this paper, an intelligent fault diagnostic tool for oil-fired power plant with turbine generator by using the modified neural network was proposed. This tool is able to monitor the running condition of power plant immediately. It also can reveal the fault situation if the power plant had some troubles. Therefore, such a well designed mechanism can be used as the training tool for laboratory course in power turbine studies. To demonstrate the feasibility of the tool we developed, several real case studies were simulated. From the simulation results shown, the tool we developed is very promising in the real applications.
Keywords :
fault diagnosis; neural nets; power engineering computing; steam power stations; turbogenerators; intelligent fault diagnostic tool; neural model; oil-fired power plants; turbine generators; Fault detection; Fault diagnosis; Intelligent networks; Learning systems; Neural networks; Neurons; Power engineering and energy; Power generation; Signal processing; Turbines;
Conference_Titel :
Innovative Computing Information and Control, 2008. ICICIC '08. 3rd International Conference on
Conference_Location :
Dalian, Liaoning
Print_ISBN :
978-0-7695-3161-8
Electronic_ISBN :
978-0-7695-3161-8
DOI :
10.1109/ICICIC.2008.151
