Abstract
For safety critical systems in nuclear power plant (NPP), accurate classification of multiclass transient leads to safer operation of the plant. Supervised machine learning is a key technique which solves multiclass classification related problems. The most widely used multiclass supervised machine learning methods for this purpose are k-nearest neighbor algorithm, support vector machine algorithm and artificial neural network (ANN) algorithm. This paper describes a comparative study on the performance of these algorithms towards classifying some of the transients in NPP. The performance analysis is mostly based on the prediction accuracy in classifying the correct transient occurred. Along with prediction accuracy, total number of epochs, training time and root mean square error was also observed as a characteristic feature for determining the performance of any backpropagation ANN. A 10-fold cross validation was carried on all these algorithms for ten times and the best among them was finally concluded for multiclass transient classification in NPP.
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Acknowledgement
The authors express their sincere thanks to the PFBR Operator Training simulator (KALBR-SIM) team members and Shri S.A.V. Satya Murty, Director, EIRSG, IGCAR for providing constant guidance and support in completing this research. The authors are greatly indebted to the constant support and motivation provided by Dr. P.R. Vasudeva Rao, Director, IGCAR.
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Prusty, M.R., Chakraborty, J., Jayanthi, T., Velusamy, K. (2015). Performance Comparison of Supervised Machine Learning Algorithms for Multiclass Transient Classification in a Nuclear Power Plant. In: Panigrahi, B., Suganthan, P., Das, S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2014. Lecture Notes in Computer Science(), vol 8947. Springer, Cham. https://doi.org/10.1007/978-3-319-20294-5_10
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DOI: https://doi.org/10.1007/978-3-319-20294-5_10
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