Application of fuzzy logic for power change rate constraint in core power control at Reaktor TRIGA PUSPATI

Abstract

The 1MWth Reaktor TRIGA PUSPATI (RTP) in Malaysia Nuclear Agency has been in operation more than 37 years. The existing core power control uses a conventional controller known as Feedback Control Algorithm (FCA). It is technically challenging to keep the core power output stable and operate within the acceptable error bands for the safety demand of the RTP. At present, the power tracking performance of the system could be considered unsatisfactory where constant gains of power change rate constraint and control rod speed constraint are used. Hence, a study of a new power change rate constraint design to achieve safe control rod speed range is conducted to improve the current performance. In this paper, a new power change rate constraint (PCRC) method using fuzzy logic is proposed to control the core power. The Takagi-Sugeno (T-S) type Fuzzy model is chosen due to its capability to work well with linear controller and making the computational control algorithm efficient. The model for core power control consists of mathematical models of the reactor core, FCA, and control rods selection algorithm. The mathematical models of the reactor core are based on point kinetics model, thermal-hydraulic models and reactivity models. The performance of power tracking and actuation signal for control rod drive input are compared between the conventional PCRC (cPCRC) and Fuzzy PCRC using MATLAB. In conclusion, the proposed Fuzzy PCRC has satisfactory performance in core power tracking for controlling the nuclear reactor with high reliability and safety.

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

“…The FCA consist of PCRC, signal filter, PI controller, cCRVD, and CRDM. The PI controller with a signal filter is designed as follows [9]; [15]:…”

“…The proposed value of wi has been assigned to each rule based on a set of inputs consisting of the rate of power change with their corresponding output values in [9] for the triangular MFs Fuzzy PCRC. The rules can be expressed as…”

“…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;…”

“…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. However, the result produced is not fully optimized in terms of settling and rise time.…”

“…However, the result produced is not fully optimized in terms of settling and rise time. Hence, in this paper, the model-based controller using Model Predictive Control (MPC) and combination Fuzzy PCRC of the strategies in [9] is studied and analyzed to further improve the power tracking performance of the RTP.…”