scholarly journals PARTICLE SWARM OPTIMIZATION BASED PROBABILISTIC NEURAL NETWORK FOR CLASSIFICATION OF SEVERE ACCIDENT OF NUCLEAR REACTOR

2021 ◽  
Vol 23 (3) ◽  
pp. 99
Author(s):  
Yoyok Dwi Setyo Pambudi
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Nuclear Reactor ◽  
Probabilistic Neural Network ◽  
Particle Swarm ◽  
Severe Accident ◽  
Swarm Optimization

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.

scholarly journals Nuclear Power Plant Construction Scheduling Problem with Time Restrictions: A Particle Swarm Optimization Approach

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Shang-Kuan Chen ◽  
Yen-Wu Ti ◽  
Kuo-Yu Tsai
Keyword(s):  
Particle Swarm Optimization ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Particle Swarm ◽  
Scheduling Problem ◽  
Swarm Optimization ◽  
Construction Scheduling ◽  
Plant Construction ◽  
Reactor Installation

In nuclear power plant construction scheduling, a project is generally defined by its dependent preparation time, the time required for construction, and its reactor installation time. The issues of multiple construction teams and multiple reactor installation teams are considered. In this paper, a hierarchical particle swarm optimization algorithm is proposed to solve the nuclear power plant construction scheduling problem and minimize the occurrence of projects failing to achieve deliverables within applicable due times and deadlines.

Complex lithology prediction using mean impact value, particle swarm optimization, and probabilistic neural network techniques

2020 ◽  
Vol 68 (6) ◽  
pp. 1727-1752
Author(s):  
Yufeng Gu ◽  
Zhongmin Zhang ◽  
Demin Zhang ◽  
Yixuan Zhu ◽  
Zhidong Bao ◽  
...  
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Probabilistic Neural Network ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Lithology Prediction

Diagnosis of Nuclear Power Plant Based on Probabilistic Neural Network

2014 ◽  
Vol 513-517 ◽  
pp. 3992-3995
Author(s):  
Chun Ling Xie
Keyword(s):  
Neural Network ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Power Plants ◽  
Probabilistic Neural Network ◽  
Bp Algorithm ◽  
Local Optimum ◽  
Fault Diagnosis System ◽  
Speed And Accuracy

This paper presents development of an automatic fault diagnosis system in the nuclear power plants to minimize possible nuclear disasters. Probabilistic Neural Network (PNN) overcame the shortcomings of entrapment in local optimum, slow convergence rate which was in BP algorithm. PNN is fit to diagnose the fault of nuclear power plant and has auto-adaptability. The method not only makes the original neural network smaller in terms of computation and realization, but also improves diagnosis speed and accuracy of nuclear power plant.

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