Adaptive Kalman filter-based real-time distribution estimation of unmeasured system states for an advanced passive reactor core under optimized mechanical shim control strategy

Reactor cores (RCs) serve as the heart of modern nuclear power plants (NPPs), necessicating efficient and reliabe monitoring during the load following operation. However, most of the critical system states within the RC, such as reactivity, fuel temperature, and xenon concentration, cannot be measured directly owing to safety reasons and technical restrictions; moreover, the RC exhibits extremely nonlinear and time-varying dynamics, compounded by the unknown process and measurement noises, posing significant challenges to advanced estimator design and implementation. Especially for the axial distribution estimation of these unmeasured system states, the relevant studies and reports are rare in the published literature. To this end, this paper is dedicated to achieving the real-time distribution estimation of unmeasured system states for an advanced passive reactor (AP1000)-type RC across its full operating range. A novel estimation algorithm integrating the state-of-the-art adaptive Kalman filter (AKF) technique is proposed to provide the axial distribution estimation of unmeasured system states in real-time, including delayed neutron precursor density, fuel and coolant temperatures, xenon and iodine concentrations, and reactivity, while utilizing only the available system measurements. On the other hand, this paper also further optimizes the mechanical shim (MSHIM) control strategy of the AP1000 by adopting an improved particle swarm optimization (IPSO) algorithm, which involves parameter optimizations for the first-order filters, lead-lag compensator, and differentiation-lag operator within the MSHIM framework. Simulation results indicate that (i) the optimized MSHIM control strategy using the IPSO outperforms the practically adopted approach, achieving significant improvements in control accuracy for load error and temperature error of the RC by 13.68 % and 21.31 %, respectively, while maintaining the same energy consumption, and (ii) under the proposed estimation algorithm in this paper, the estimates of system states provided by the AKF-based estimation algorithm exhibit strong agreement with their model-based values during the load following operation of the AP1000, with the maximum absolute relative error of 1.21 % merely, thereby verifying the proposed AKF-based estimation algorithm’s feasibility and accuracy.