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Energies 2017, 10(2), 173; doi:10.3390/en10020173

Parameter Identification with the Random Perturbation Particle Swarm Optimization Method and Sensitivity Analysis of an Advanced Pressurized Water Reactor Nuclear Power Plant Model for Power Systems

1
School of Electrical Engineering, Wuhan University, Wuhan 430072, China
2
State Grid Fujian Electric Power Co. Ltd., Economic and Technology Institute, Fuzhou 350012, China
*
Author to whom correspondence should be addressed.
Academic Editors: Dan Gabriel Cacuci and Enrico Sciubba
Received: 8 November 2016 / Revised: 16 December 2016 / Accepted: 22 January 2017 / Published: 4 February 2017
(This article belongs to the Special Issue Advances in Predictive Modeling of Nuclear Energy Systems)
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Abstract

The ability to obtain appropriate parameters for an advanced pressurized water reactor (PWR) unit model is of great significance for power system analysis. The attributes of that ability include the following: nonlinear relationships, long transition time, intercoupled parameters and difficult obtainment from practical test, posed complexity and difficult parameter identification. In this paper, a model and a parameter identification method for the PWR primary loop system were investigated. A parameter identification process was proposed, using a particle swarm optimization (PSO) algorithm that is based on random perturbation (RP-PSO). The identification process included model variable initialization based on the differential equations of each sub-module and program setting method, parameter obtainment through sub-module identification in the Matlab/Simulink Software (Math Works Inc., Natick, MA, USA) as well as adaptation analysis for an integrated model. A lot of parameter identification work was carried out, the results of which verified the effectiveness of the method. It was found that the change of some parameters, like the fuel temperature and coolant temperature feedback coefficients, changed the model gain, of which the trajectory sensitivities were not zero. Thus, obtaining their appropriate values had significant effects on the simulation results. The trajectory sensitivities of some parameters in the core neutron dynamic module were interrelated, causing the parameters to be difficult to identify. The model parameter sensitivity could be different, which would be influenced by the model input conditions, reflecting the parameter identifiability difficulty degree for various input conditions. View Full-Text
Keywords: primary loop system model; pressurized water reactor (PWR) units; parameter identification; sensitivity analysis primary loop system model; pressurized water reactor (PWR) units; parameter identification; sensitivity analysis
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Wang, L.; Zhao, J.; Liu, D.; Lin, Y.; Zhao, Y.; Lin, Z.; Zhao, T.; Lei, Y. Parameter Identification with the Random Perturbation Particle Swarm Optimization Method and Sensitivity Analysis of an Advanced Pressurized Water Reactor Nuclear Power Plant Model for Power Systems. Energies 2017, 10, 173.

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