Unsupervised classification of NPPs transients based on online dynamic quantum clustering

Abstract

In this study, we propose a new method for identification of nuclear power plants (NPPs) transients based on online dynamic quantum clustering (DQC). In this unsupervised learning algorithm, the Gaussian kernel is the eigen-state of the Schrodinger equation and the minimums of the Schrodinger potential are the cluster centers of patterns. For clustering of transients, data of each event are given to the DQC and form a cluster independent of other transients. This process is done for all target plant conditions. The formed clusters are labeled according to the name of their related transients. Afterwards, to test the proposed identifier, as time goes by, new data points move toward the formed potential wells. Finally, each new datum falls into an appropriate cluster and, therefore, the type of transient is identified online. The DQC, unlike previously developed unsupervised learning algorithms, is not dependent on the geometric proximity of data. The developed identifier is examined by the Iris flower dataset and typical WWER-1000 plant transients. Results show a reasonable performance of DQC. We use singular value decomposition (SVD) and bipolar representation of real data to reduce the dimensions of data and to show explicitly positive and negative sides of information. The major novelty of this identifier is the development of a technique for online transient identification of NPPs utilizing the DQC without any preliminary information about the input patterns. The developed method is a step forward for practical recognition of NPPs transients.