Enhanced Neural Network Based Fault Detection of a VVER Nuclear Power Plant With the Aid of Principal Component Analysis

This paper presents a neural network based fault diagnosing approach which allows dynamic fault identification. The method utilizes the principal component analysis (PCA) technique to dramatically reduce the problem dimension. Such a dimension reduction approach leads to faster diagnosing and allows...

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Bibliographic Details
Published in:IEEE transactions on nuclear science 2008-12, Vol.55 (6), p.3611-3619
Main Authors: Hadad, K., Mortazavi, M., Mastali, M., Safavi, A.A.
Format: Article
Language:English
Subjects:
Algorithm design and analysis
Artificial neural network (ANN)
Backpropagation algorithms
Discrete event simulation
Fault detection
Fault diagnosis
moving temporal window (MTW)
Multi-layer neural network
Multilayer perceptrons
Neural networks
Power generation
Principal component analysis
principal component analysis (PCA)
Studies
VVER 1000
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Summary:This paper presents a neural network based fault diagnosing approach which allows dynamic fault identification. The method utilizes the principal component analysis (PCA) technique to dramatically reduce the problem dimension. Such a dimension reduction approach leads to faster diagnosing and allows a better graphical presentation of the results. To show the effectiveness of the proposed approach, two methodologies are used to train the neural network (NN). At first, a training matrix composed of 15 variables is used to train a multilayer perceptron neural network (MLP) with resilient backpropagation (RP) algorithm. Employing the proposed method, a more accurate and simpler network is designed where the input size is reduced from 15 to 6 variables for training the NN. In short, the application of PCA highly reduces the network topology and allows employing more efficient training algorithms. The developed networks use, as input, a short set (in a moving temporal window (MTW)) of recent measurements of each variable avoiding the necessity of using starting events. The accuracy, generalization ability and reliability of the designed networks are verified using 10 simulated events data from a VVER-1000 simulator. Noise is added to the data to evaluate robustness of the method, and the method again shows to be effective and powerful.
ISSN:0018-9499
1558-1578
DOI:10.1109/TNS.2008.2006491