Adaptive Unscented Kalman Filtering for Reactivity Estimation in Nuclear Power Plants

摘要

Reactivity is a key parameter in nuclear power plants (NPPs). It reflects the balance between neutron generation and consumption inside the reactor core. Therefore, reactivity monitoring inside the reactor core is essential to ensure the safe operation of NPPs. However, no physical sensor is available for the measurement of reactivity. It can be either inferred indirectly from the reactor period or estimated using the reactor flux variation. The inference of reactivity from the reactor period has its own limitations. Therefore, reactor flux-based reactivity computation is of more interest. Various techniques based on deterministic as well as stochastic approaches for online computation of reactivity using the reactor flux are reported in the literature. In this article, a Rao-Blackwellised unscented Kalman filter (RBUKF)-based adaptive state estimator is proposed for the online estimation of reactivity from the output signal of neutron detectors. The efficacy of the proposed adaptive RBUKF over nonadaptive RBUKF is established through simulations under different transient scenarios.