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

Li Wang, Jie Zhao, Dichen Liu, Yi Lin, Yu Zhao, Zhangsui Lin, Ting Zhao and Yong Lei
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Li Wang: School of Electrical Engineering, Wuhan University, Wuhan 430072, China
Jie Zhao: School of Electrical Engineering, Wuhan University, Wuhan 430072, China
Dichen Liu: School of Electrical Engineering, Wuhan University, Wuhan 430072, China
Yi Lin: State Grid Fujian Electric Power Co. Ltd., Economic and Technology Institute, Fuzhou 350012, China
Yu Zhao: School of Electrical Engineering, Wuhan University, Wuhan 430072, China
Zhangsui Lin: State Grid Fujian Electric Power Co. Ltd., Economic and Technology Institute, Fuzhou 350012, China
Ting Zhao: School of Electrical Engineering, Wuhan University, Wuhan 430072, China
Yong Lei: State Grid Fujian Electric Power Co. Ltd., Economic and Technology Institute, Fuzhou 350012, China

Energies, 2017, vol. 10, issue 2, 1-22

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.

Keywords: primary loop system model; pressurized water reactor (PWR) units; parameter identification; sensitivity analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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