Title :
Fault detection of gas unit of Gilan combined cycle power plant using neural network
Author :
Forootani, Ali ; Yazdizadeh, A. ; Aliabadi, A.
Abstract :
Fault detection is one of the most important and challenging issues in engineering application. In this article fault detection of Gilan combined cycle power plant is investigated. To do so two neural network structures are applied. The first neural network which is trained by Kalman Filter. The second structure is NARX network which is trained by levenberg-marquardt method. The results obtained show that the neural network has a great capability in fault detection.
Keywords :
Kalman filters; combined cycle power stations; fault diagnosis; learning (artificial intelligence); neural nets; power engineering computing; power generation faults; Gilan combined cycle power plant; Kalman filter; Levenberg-Marquardt method; NARX network; engineering application; fault detection; gas unit; neural network structure; training; Biological neural networks; Fault detection; Fuels; Gases; Turbines; Fault detection; Kalman Filter; NARX; combined cycle power plant;
Conference_Titel :
Thermal Power Plants (CTPP), 2011 Proceedings of the 3rd Conference on
Conference_Location :
Tehran
Print_ISBN :
978-1-4799-0591-1
