Yoyok Dwi Setyo Pambudi



Due to its danger and complexity, the identification and prediction of major severe accident scenarios from an initiating event of a nuclear power plant remains a challenging task. This paper aims to classify severe accident at the Advanced Power Reactor (APR) 1400, which includes the loss of coolant accidents (LOCA), total loss of feedwater (TLOFW), station blackout (SBO), and steam generator tube rupture (SGTR) using a standard  probabilistic neural network (PNN)  and Particle Swarm Optimization Based Probabilistic Neural Network (PSO PNN). The algorithm has been implemented in MATLAB.  The experiment results showed that supervised PNN PSO could classify severe accident of nuclear power plant better than the standar PNN.

Full Text:



  1. Santoso S., Himawan R., Situmorang J., Suryono T.J., Edison E. Reactor Operational Experience Review and Analysis Based on Un-intended Reactor Trip Data. J. Teknol. Reakt. Nukl. TRI DASA MEGA. 2019. 21(2):71-78.
  2. No Y.-G., Kim J.-H., Na M.-G., Lim D.-H., Ahn K.-I. Monitoring Severe Accidents Using AI Techniques. Nucl. Eng. Technol. 2012. 44(4):393-404.
  3. Kim S.H., Shin S.G., Han S., Kim M.H., Pyeon C.H. Feasibility Study on Application of an Artificial Neural Network for Automatic Design of a Reactor Core at the Kyoto University Critical Assembly. Prog. Nucl. Energy. 2020. 119:103183.
  4. Zhao Y., Li T., Zhang X., Zhang C. Artificial Intelligence-based Fault Detection and Diagnosis Methods for Building Energy Systems: Advantages, Challenges and the Future. Renew. Sustain. Energy Rev. 2019. 109:85-101.
  5. Tian D., Deng J., Vinod G., Santhosh T. V, Tawfik H. A Constraint-based Genetic Algorithm for Optimizing Neural Network Architectures for Detection of Loss of Coolant Accidents of Nuclear Power Plants. Neurocomputing. 2018. 322:102-109.
  6. Lee D., Seong P.H., Kim J. Autonomous Operation Algorithm for Safety Systems of Nuclear Power Plants by Using Long-short Term Memory and Function-based Hierarchical Framework. Ann. Nucl. Energy. 2018. 119:287-299.
  7. Shi J., Deng Y., Wang Z. Analog Circuit Fault Diagnosis Based on Density Peaks Clustering and Dynamic Weight Probabilistic Neural Network. Neurocomputing. 2020. 407:354-365.
  8. Santhosh T. V, Gopika V., Ghosh A.K., Fernandes B.G. An Approach for Reliability Prediction of Instrumentation & Control Cables by Artificial Neural Networks and Weibull Theory for Probabilistic Safety Assessment of NPPs. Reliab. Eng. Syst. Saf. 2018. 170:31- 44.
  9. Zhu H., Lu L., Yao J., Dai S., Hu Y. Fault Diagnosis Approach for Photovoltaic Arrays Based on Unsupervised Sample Clustering and Probabilistic Neural Network Model. Sol. Energy. 2018. 176:395 -405.
  10. Gao W., Zhao Y., Smidts C. Component Detection in Piping and Instrumentation Diagrams of Nuclear Power Plants Based on Neural Networks. Prog. Nucl. Energy. 2020. 128:103491.
  11. Liu J., Seraoui R., Vitelli V., Zio E. Nuclear Power Plant Components Condition Monitoring by Probabilistic Support Vector Machine. Ann. Nucl. Energy. 2013. 56:23-33.
  12. Dos Santos M.C., Pinheiro V.H.C., Do Desterro F.S.M., De Avellar R.K., Schirru R., Dos Santos Nicolau A., et al. Deep Rectifier Neural Network Applied to the Accident Identification Problem in a PWR Nuclear Power Plant. Ann. Nucl. Energy. 2019. 133:400-408.
  13. Zhao Y., Tong J., Zhang L. Rapid Source Term Prediction in Nuclear Power Plant Accidents Based on Dynamic Bayesian Networks and Probabilistic Risk Assessment. Ann. Nucl. Energy. 2021. 158:108217.
  14. Norouzi N., Sadegh-Amalnick M., Alinaghiyan M. Evaluating of the Particle Swarm Optimization in a Periodic Vehicle Routing Problem. Measurement. 2015. 62:162-169.
  15. Augusto J.P. da S.C., Dos Santos Nicolau A., Schirru R. PSO with Dynamic Topology and Random Keys Method Applied to Nuclear Reactor Reload. Prog. Nucl. Energy. 2015. 83:191-196.
  16. Jamalipour M., Gharib M., Sayareh R., Khoshahval F. PWR Power Distribution Flattening Using Quantum Particle Swarm Intelligence. Ann. Nucl. Energy. 2013. 56:143-150.
  17. Pambudi Y.D.S., Wahab W., Kusumoputro B. Particle Swarm Optimization-Based Direct Inverse Control for Controlling the Power Level of the Indonesian Multipurpose Reactor. Sci. Technol. Nucl. Install. 2016. 2016:1- 9.
  18. Coban R. Power Level Control of the TRIGA Mark-II Research Reactor Using the Multifeedback Layer Neural Network and the Particle Swarm Optimization. Ann. Nucl. Energy. 2014. 69:260-266.
  19. Jiang Y., Li X., Huang C., Wu X. Application of Particle Swarm Optimization Based on CHKS Smoothing Function for Solving Nonlinear Bilevel Programming Problem. Appl. Math. Comput. 2013. 219(9):4332 - 4339.


  • There are currently no refbacks.

PTKRN Digital Library Mendeley