Design of a model predictive control method for load tracking in nuclear power plants

Wang, Guoxu; Zeng, Bifan; Xu, Zhibin; Wu, Wanqiang; Ma, Xiaoqian

doi:10.1016/j.pnucene.2017.08.012

Cited by 41 publications

(8 citation statements)

References 30 publications

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“…In addition, MPC is also used in the field of nuclear reactor control. Wang et al (2017) proposed QPbased MPC and verified the effectiveness in load following of pressurized water reactor by numerical simulations. Li et al (2019) used a multivariable control method of pressure and water level based on MPC to achieve stable tracking of the setpoint accurately under the simultaneous change of pressure and water level setpoints.…”

Section: Introductionmentioning

confidence: 88%

Model predictive power control of a heat pipe cooled reactor

Huang

Sun

2023

Front. Energy Res.

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Heat pipe cooled reactor (HPR) has broad application prospects in deep space exploration, deep-sea submarine exploration, and other scenarios due to the small size, high inherent safety, and easy modularization and expansion. However, the HPR conducts thermal energy through evaporation and condensation of the working fluid inside the heat pipe. This feature makes the HPR a large time-delay system. If the power control system adopts the conventional PID algorithm, there will be a long settling time. Therefore, the model predictive control algorithm is proposed for the power control system to improve the control performance. The HPR linear model, which is developed by linearization of its nonlinear model, is chosen as the predictive model. The optimal control value is obtained by solving the optimization problem based on the predictive model and the electric power feedback value. The discrepancy between the predive model and the actual system response results in the presence of steady-state error. To solve this problem, an integral controller is added to eliminate the error. The appropriate control system parameters are tuned by the trial and error method. The proposed control system has satisfactory control performance, which can significantly shorten the settling time. The model predictive control can effectively overcome the influence of the large time-delay characteristic.

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Section: Introductionmentioning

confidence: 88%

Model predictive power control of a heat pipe cooled reactor

Huang

Sun

2023

Front. Energy Res.

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“…In general, the controlled quality is defined as the velocity of the control rod that can be considered as 𝑢 = 0 when the actual core power is stable at the desired power level. Thus, the objective cost function J for the MPC system is defined as [18]: 𝐽 = (𝑅 𝑠 − 𝑌) 𝑇 (𝑅 𝑠 − 𝑌) + 𝑈 𝑇 𝑅 𝑊 𝑈 (9) where 𝑌 = [𝑦(𝑘 + 1) 𝑦(𝑘 + 2) ⋯ 𝑦(𝑘 + 𝑁 𝑝 )] 𝑇 ; 𝑅 𝑠 = [ 0.75 0.75 ⋯ 0.75 ] 𝑇 𝑟(𝑘); r is the reference trajectory (power demand) of core power; 𝑈 = [𝑢(𝑘) 𝑢(𝑘 + 1) ⋯ 𝑢(𝑘 + 𝑁 𝑐 − 1)] 𝑇 ; Np is the prediction horizon; Nc is the control horizon;…”

Section: Triga Model Predictive Core Power Controlmentioning

confidence: 99%

Model predictive and fuzzy logic controllers for reactor power control at Reaktor TRIGA PUSPATI

Minhat

Subha

Hassan

et al. 2022

IOP Conf. Ser.: Mater. Sci. Eng.

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The Reaktor TRIGA PUSPATI (RTP) rely extensively on a core power control system for control reactivity to fulfilment the fundamental safety function for a nuclear reactor. It is technically challenging to operate within tight multiple parameter constraints and keep the core power output stable. At present, the power tracking performance of the system could be considered unsatisfactory which produces a relatively long settling time and high control effort. Hence, a study of a model predictive control (MPC) strategy by integrating the current Power Change Rate Constraint (PCRC) using fuzzy logic which is part of the core power control design is conducted. In this paper, the MPC design based on mathematical models of the reactor core included point kinetics model, thermal-hydraulic model, reactivity model and dynamic rod position model help to enhance core power control. The power tracking performance of the proposed control method and previous Feedback Control Algorithm-Fuzzy PCRC is compared via computer simulation with different power range operations. Overall, the results show the MPC-Fuzzy PCRC strategy provides a greater level of operational safety and optimum operation of the TRIGA reactor.

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“…The reactivity equation for TRIGA in Equation. (4) is expressed as the sum of reactivity due to control rod movement, reactivity due to two temperature feedback [19], [34][35], and for long time operation; reactivity due to xenon concentration feedback [36] need to be included.…”

Section: Modelling Of Triga Puspati Reactormentioning

confidence: 99%

An improved control rod selection algorithm for core power control at TRIGA PUSPATI Reactor

Minhat

Subha

Hassan

et al. 2020

JMES

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The 1 MWth TRIGA PUSPATI Reactor known as RTP undergoes more than 37 years of operation in Malaysia. The current core power control utilized Feedback Control Algorithm (FCA) and a conventional Control Rod Selection Algorithm (CRSA). However, the current power tracking performance suffers and increase the workload on Control Rod Drive Mechanism (CRDM) if the range between minimum and maximum rod worth value for each control rod has a significant difference. Thus, it is requiring much time to keep the core power stable at the power demand value within the acceptable error bands for the safety requirement of the RTP. In conventional CRSA, regardless of the rod worth value, the lowest position of the control rod is selected for up-movement to regulate the reactor power with 2% chattering error. To improve this method, a new CRSA is introduced named Single Control Absorbing Rod (SCAR). In SCAR, only one rod with highest reactivity worth value will be selected for coast tuning during transient and the lowest reactivity worth value will be selected for fine-tuning rod movement during steady-state. The simulation model of the reactor core is represented based on point kinetics model, thermal-hydraulic models and reactivity model. The conventional CRSA model included with control rod position dynamic model and actual reactivity worth curve data from RTP. The FCA controller is designed based on Proportional-Integral (PI) controller using MATLAB Simulink simulation. The core power control system is represented by the integration of a reactor core model, CRSA model and FCA controller. To manifest the effectiveness of the proposed SCAR algorithm, the results are compared to the conventional CRSA in both simulation and experimentation. Overall, the results shows that the SCAR algorithm offers generally better results than the conventional CRSA with the reduction in rising time up to 44%, workload up to 35%, settling time up to 26% and chattering error up to 18% of the nominal value.

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Design of a model predictive control method for load tracking in nuclear power plants

Cited by 41 publications

References 30 publications

Model predictive power control of a heat pipe cooled reactor

Model predictive power control of a heat pipe cooled reactor

Model predictive and fuzzy logic controllers for reactor power control at Reaktor TRIGA PUSPATI

An improved control rod selection algorithm for core power control at TRIGA PUSPATI Reactor

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