By Topic

An Online Fault Diagnosis Method for Nuclear Power Plant Based on Combined Artificial Neural Network

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Ren Yu ; Navy Univ. of Eng., Wuhan, China ; Feng Liu

An online fault diagnosis method based on a hybrid artificial neural network (ANN) for nuclear power plant (NPP) is proposed in the paper. It adopts the BP ANN for a quickly group pre-diagnosis at first, then uses the RBF ANNs to verify the results of the BP ANN. Several simulation experiments are carried out using a NPP simulator while the NPP is under different operating conditions. The results show that the proposed method can not only diagnose the learned faults quickly and accurately, but also identify the unlearned faults under different operating conditions, even with noise signal in the input data. The output of the diagnosis system is a list of the possible faults with their probabilities. This makes the diagnosis result be more understandable and acceptable for the operator of NPP.

Published in:

Power and Energy Engineering Conference (APPEEC), 2010 Asia-Pacific

Date of Conference:

28-31 March 2010