Spatial control of a large pressurized heavy water reactor by fast output sampling technique

Bandyopadhyay, B.; Tiwari, A.P.

doi:10.1109/tns.2003.818271

Cited by 31 publications

(29 citation statements)

References 15 publications

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“…A multi-model feedback control with a state observer was proposed by Wu et al [15] to carry out power regulations of a PWR core. Sharma et al [16] developed the state-feedback based controller to control the power of a large PWR core through the introduction of an output sampling technique and a LMI formulation. A state-feedback controller is contrived by Xia et al [17] to obtain desired power distributions of a CANDU reactor core based on the linear quadratic regulator control.…”

Section: Feedback Control With State Observermentioning

confidence: 99%

“…Improving power regulation technology of cores by the introduction of control algorithms is an important measure for safety and availability of NPPs. Over the decades, many control algorithms have been exploited and applied by researchers to core power regulations, which are the stateor output-feedback control with a state observer [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17], the optimal control [18,19], the neural network or fuzzy intelligent control [20][21][22][23][24][25], the model predictive control [26][27][28], the H ∞ robust control [29][30][31], the sliding model control [32][33][34][35], the fractional order control [36][37][38][39][40][41] and other control algorithms [42][43][44][45][46][47]…”

Section: Introductionmentioning

confidence: 99%

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Review on Application of Control Algorithms to Power Regulations of Reactor Cores

Wang

Liang

et al. 2016

ITM Web Conf.

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Abstract. This research is to solve the stability analysis issue of nonlinear pressurized water reactor cores. On the basis of modeling a nonlinear pressurized water reactor core using the lumped parameter method, its linearized model is achieved via the small perturbation linearization way. Linearized models of the nonlinear core at six power levels are selected as local models of this core. The T-S fuzzy idea for the core is exploited to construct the T-S fuzzy model of the nonlinear core based on the local models and the triangle membership function, which approximates the nonlinear core model within the entire range of power level. This fuzzy model as a bridge is to cater to the stability analysis of the nonlinear core after defining its stability. One stability theorem is deduced to define the nonlinear core is globally asymptotically stable in the global range of power level. Finally, the simulation work and stability analyses are separately completed. Numerical simulations show that the fuzzy model can substitute the nonlinear core model; stability analyses verify the nonlinear core is globally asymptotically stable in the global range of power level.

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Section: Feedback Control With State Observermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Review on Application of Control Algorithms to Power Regulations of Reactor Cores

Wang

Liang

et al. 2016

ITM Web Conf.

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“…The reactor, considered in this work, produces approximately 1800 MW thermal power. It is a 70th order Multi-Input Multi-Output (MIMO) system, which is divided into 14 zones for the purpose of controlling and observing the neutron flux (Khan, 2009;Sharma et al, 2003;Tiwari et al, 2000). There are various in-core and ex-core instrumentation through which the neutron flux can be monitored.…”

Section: Phwr Mimo Modelmentioning

confidence: 99%

“…Furthermore, various control schemes require the full state feedback which is another limitation as the instruments to measure the states like Iodine and Xenon concentrations in nuclear reactors, are not available. The immeasurable states in such control schemes therefore have to be estimated by using observers which make the system more complex (Sharma et al, 2003;Tiwari et al, 2000).…”

Section: Introduction and Related Workmentioning

confidence: 99%

Design and tuning of a Decentralized Fuzzy Logic Controller for a MIMO type Pressurized Heavy Water Reactor

Wallam

Abbas

Abbasi

2015

Annals of Nuclear Energy

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“…Design of a controller based on fast output sampling technique for a large PHWR by converting it into block diagonal form and then decomposing into a fast subsystem and a slow subsystem, has been demonstrated [16]. State feedback controls are designed separately for the slow subsystem and the fast subsystem and then a composite state feedback control is obtained.…”

Section: Periodic Output Feedback Designmentioning

confidence: 99%

Spatial Control of a Large PHWR by Decentralized Periodic Output Feedback and Model Reduction Techniques

Talange

Bandyopadhyay

Tiwari

2006

IEEE Trans. Nucl. Sci.

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Abstract-Nuclear reactors of small and medium size are generally described by point kinetics model, however, this model is not valid in case of a large reactor, because in that flux shape undergoes appreciable variation with time. The behaviour of large reactor core can be explained with reasonable accuracy by spatial model like nodal model, which considers the reactor to be divided into number of regions or nodes. The thermal feedbacks which introduce nonlinearity into the problem, should be considered for realistic modeling. The spatial model of 540-MWe PHWR developed in [4] is augmented with the dynamics of coolant and fuel temperatures and a 72nd-order model is obtained. As working with such a large model is difficult from the point of view of numerical computations, the new model having 14 inputs and 14 outputs, is suitably reduced by aggregation technique to obtain a 26th-order reduced model, which is more suitable to handle. The design of spatial controller by a state feedback based on reduced model needs the availability of all the states of the system for feedback purpose. As all the states of the reactor are not accessible for measurement, one has to resort to output feedback. Also, as the stability is not guaranteed by static output feedback, here the spatial controller is designed by periodic output feedback which is static in nature and at the same time guarantees complete closed-loop pole assignability. The various zones in a large reactor are coupled and a change in the control input to any zone causes respective change in the neutron flux of the other neighbouring zones, which may not be desirable. Therefore, a decentralized controller would serve as a better option, as it ensures that the input to any zone affects corresponding zone only and other zones are not affected by it. The above idea of periodic output feedback controller design yields a gain matrix with large magnitude, which amplifies measurement noise and is difficult to implement practically. Hence, it is desirable to keep the gain low. This objective is suitably expressed as LMI problem and putting appropriate design constraints, a better gain is obtained.The nonlinear model of the 540-MWe PHWR with the controller as above is tested for the reactivity transient simulation and the results of such a simulation are presented.Index Terms-Aggregation, decentralized systems, discrete systems, large scale systems, periodic output feedback, pole assignment, reactivity, reduced-order model, spatial control.

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Spatial control of a large pressurized heavy water reactor by fast output sampling technique

Cited by 31 publications

References 15 publications

Review on Application of Control Algorithms to Power Regulations of Reactor Cores

Review on Application of Control Algorithms to Power Regulations of Reactor Cores

Design and tuning of a Decentralized Fuzzy Logic Controller for a MIMO type Pressurized Heavy Water Reactor

Spatial Control of a Large PHWR by Decentralized Periodic Output Feedback and Model Reduction Techniques

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