Classification of NPPs transients using change of representation technique: A hybrid of unsupervised MSOM and supervised SVM

Abstract

This study introduces a new identifier for nuclear power plants (NPPs) transients. The proposed identifier changes the representation of input patterns. Change of representation is a semi-supervised learning algorithm which employs both of labeled and unlabeled input data. In the first step, modified self-organizing map (MSOM) carries out an unsupervised learning algorithm on labeled and unlabeled patterns and generates a new metric for input data. In the second step, support vector machine (SVM) as a supervised learning algorithm classifies the input patterns using the generated metric of the first step. In contrast to unsupervised learning algorithms, the proposed identifier does not discard the collected information. The proposed identifier is examined by the Iris flower dataset and the Bushehr nuclear power plant (BNPP) transients. In overall, results show good performance of the developed identifier. Training with small fraction of labeled patterns, classification only by the sign of the classifiers outputs, and modular identification are main advantages of the proposed identifier. Results indicate that the developed MSOM is not able to cluster correctly quasi-static transients such as uncontrolled withdrawal of control rods (UWCR) in presence of steady state patterns. Quasi-static transients are very similar to steady state. Moreover, imposed noise on input data may eliminate minor differences among the patterns of them. This may cause wrong training of SVM. This is the most important challenge of semi-supervised learning. In other words, if the knowledge on density of unlabeled input patterns do not carry useful information for prediction of the targets of unlabeled patterns, then semi-supervised learning may degrade the prediction accuracy. Therefore, the proposed identifier is more appropriate for classification of transients in which either clusters have been apart or small noises have been imposed on data.