Research on multi-variable fuzzy predictive control based on integrated model of secondary system of the nuclear power plant

In a nuclear power plant there are two major equipment with high mechanical inertia, which have a rotating shaft. These are Pumps in the Primary Heat Transport System and Turbine in the secondary system. In both cases, the shaft seizure leads to transfer of very large loads to the supports. These supports, if not designed for seizure loads may fail. If the supports fail, there is a good possibility of a missile generated and hit the safety equipment. Seizure loads in these machines have three components namely mechanical inertial load, electrical load and hydraulic load. While the electrical and hydraulic loads have a limited peak value, the inertial load depends on the seizure time. For the normal observed seizures the three have a similar order of magnitude during seizure. As the casing is overdesigned the combined load is experienced by the supports. The pump of the Primary Heat Transport System (PHTS) of a nuclear power plant is centrifugal type run by an induction motor. If the pump shaft seizes, the seizure load will be experienced by the support structure. Due to the presence of the flywheel, the total moment of inertia of the pump motor assembly is quite high. Hence the resisting torque may be higher than the support’s design torque. Besides, the electric torque will continue to be applied as the motor trip on the overload current is delayed by several seconds as the corresponding relay is a thermal relay. Seizure torque will depend on pump seizure time. Lesser the seizure time, higher would be the load on the pump supports. The turbine in the secondary system has a large inertia due to blades. In case of a seizure the generator is tripped in hundreds of milliseconds. The load experienced by turbine supports due to seizure is significantly enhanced in the first few seconds due to sustained steam supply before it is cut off. This paper discusses the estimation of the three types of loads during seizure of the shafts in the pumps and turbine. It also discusses the possible safety consequences of these loads.

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Nonlinear model predictive control based on T-S fuzzy model for a PWR nuclear power plant

The 26th Chinese Control and Decision Conference (2014 CCDC) ◽

10.1109/ccdc.2014.6852508 ◽

2014 ◽

Cited By ~ 2

Author(s):

Mengyue Wang ◽

X. J. Liu

Keyword(s):

Power Plant ◽

Nuclear Power Plant ◽

Model Predictive Control ◽

Predictive Control ◽

Nonlinear Model ◽

Nuclear Power ◽

Fuzzy Model ◽

Nonlinear Model Predictive Control

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Coupling of RELAP and LabVIEW to Model a Nuclear Power Plant System Realistically

Volume 6B: Energy ◽

10.1115/imece2014-36674 ◽

2014 ◽

Author(s):

J. C. Pack ◽

Z. Fu ◽

F. Aydogan

Keyword(s):

Power Plant ◽

Nuclear Power Plant ◽

Virtual Instrument ◽

Nuclear Power ◽

System Modeling ◽

Loop Model ◽

Secondary System ◽

Loss Of Coolant Accident ◽

Accident Scenarios ◽

Computer Simulators

Within the study and design of a nuclear power plant extensive system modeling is necessary to determine how the reactor is going to perform in any given situation, not only in the normal performance of the reactor but also transients including unanticipated transients without scram and hypothetical accidents. One of the difficulties in the performance of this modeling is that there are often separate programs used to model the primary and other loops in multiple loop systems. When the modeling requires no interaction between the loops, this method is adequate but in many of these scenarios an understanding of the interaction loops is crucial especially in the case of transients including accident scenarios. However, each loop is generally modeled individually and there is no feedback effect between loops. The purpose of this article is to demonstrate how this coupling between the primary and secondary system of a typical PWR can be performed. The primary and secondary sides of the PWR are modeled with Reactor Excursion and Leak Analysis Program (RELAP5) and Laboratory Virtual Instrument Engineering Workbench (LabVIEW) computer simulators respectively. Primary loop model includes a four loops PWR. The coupling between RELAP5 and LabVIEW has been executed with steady state and transients, in this case a loss of coolant accident (LOCA). The results of the coupling have been compared with the typical RELAP5 results without coupling.

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Dynamic Simulation on Secondary System of Nuclear Power Plant

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.591-593.620 ◽

2012 ◽

Vol 591-593 ◽

pp. 620-625 ◽

Cited By ~ 1

Author(s):

Rui Yu ◽

Shi Wei Yao ◽

Chun Guo Wang

Keyword(s):

Steady State ◽

Power Plant ◽

Nuclear Power Plant ◽

Dynamic Simulation ◽

Nuclear Power ◽

Large Scale ◽

Simulation Models ◽

Secondary System ◽

Actual System ◽

Moisture Separator

The secondary system of Qinshan phase I nuclear power plant is simulated in this study. According to the characteristics of the JTopmeret model, the system is divided into six parts for modeling, which are the deaerator, the high pressure (HP) turbine, the low pressure (LP) turbine, the moisture separator reheater (MSR), the condensate system, and the feedwater system. All parts are built as the graphic automatic models in JTopmeret and debugged on the large-scale simulation platform GSE to complete the steady-state and dynamic simulation of the models. The results show that the steady-state and dynamic processes of the models are consistent with the characteristics of the actual system. It verifies the correctness of the simulation models. Thus, this research is able to provide guidance for the operation analysis and the equipment debugging of the secondary system of the nuclear power plant.

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