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

Abstract

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.

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

“…( 2) presented in Section 2. The validation of the RTP simulation model using the experimental data has been reported in our previous work [8]; [10]. The control horizon is determined by the rule of thumb in the Matlab simulation manual [20], in which the control horizon is set to 10% to 20% of the prediction horizon and has a minimum of 2 to 3 steps.…”

“…where In order to have high consistency and accuracy in the RTP model included the control rod position dynamic in [8] can also be derived based on a set of actual input and output data using System ID. The plant model's linearized state-space model based on System ID can be represented as [16]…”

“…Then, Eq. ( 7) can be rewritten as: { 𝑥(𝑘 + 1) = 𝐴𝑥(𝑘) + 𝐵𝑢(𝑘) 𝑦(𝑘 + 1) = 𝐶𝑥(𝑘 + 1) + 𝐷𝑢(𝑘) (8) where the variables are considered as the deviations from the nominal values based on the operating zone where the model is defined.…”

“…The RTP uses a conventional core power control known as Feedback Control Algorithm (FCA) [7]; [8] for the power manoeuvring up to 1 MWth based on the conventional Control Rod Velocity Design (cCRVD) and conventional Power Change Rate Constraint (cPCRC). In our recent works, the new PCRC based on the fuzzy logic approach [9] and changes in the maximum rod speed limiter values [10] have been designed to provide better results than the FCA-conventional PCRC method in terms of reducing chattering error.…”