Fractional Order Neural Transient Modeling of Primary Circuit of ACP1000 Based Nuclear Power Plant in LabVIEW
Fractional Order Neural ACP1000 Dynamics
DOI:
https://doi.org/10.53560/PPASA(58-4)759Keywords:
Fractional Order, Neural Estimation, Primary Circuit, Coupled Systems, ACP1000, Nuclear Power Plant, LabVIEWAbstract
The primary circuit of the nuclear power plant is the most advanced and sophisticated loop of the Advanced Chinese Pressurized Water Reactor (ACP1000). The primary circuit is composed of most technologically advanced nuclear systems and controllers. In this research work, closed loop dynamics of primary circuit (CLPC) of ACP1000 based nuclear power plant is identified. The closed loop dynamics is comprised of highly nonlinear coupled sevencontrol systems. The turbine power, pressurizer temperature, cold leg temperature, hot leg temperature, coolant average temperature and feed water flow are the selected parameters of interest as inputs while neutron power, reactor coolant pressure, pressurizer level, steam generator pressure, steam generator level and steam generator flow as outputs. Therefore, a closed loop multi-input multi-out (MIMO) is configured. The control oriented closed loop dynamics of the primary circuit of ACP1000 is estimated by state-of-the-art novel fractional order neural network (FO-ANN) tool developed in LabVIEW. The parameters of FO-ANN of CLPC (FO-ANN-CLPC) are optimized using fractional order backpropagation (FO-BP) algorithm. The performance of FO-ANN-CLPC is tested and validated in transient conditions and the proposed model predicted the desired reactor power with minimizing error function. The robust performance of the proposed closed loop model is evaluated by dynamic simulation for a prescribed turbine load power increase transient from 20 % to 100 % and validated against reactor power and behaviour of various thermal hydraulics parameters are observed and analyzed.
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