Estimation, Analysis and Smoothing of Self-Similar Network Induced Delays in Feedback Control of Nuclear Reactors

TL;DR

This paper investigates the self-similar, fractional-order network delays affecting nuclear reactor power signals, analyzing their properties and proposing smoothing techniques to mitigate their impact on control systems.

Contribution

It introduces a detailed analysis of self-similar network delays in nuclear reactor feedback control and evaluates smoothing filters tailored for fractional-order noise.

Findings

Delay dynamics exhibit self-similarity and fractional behavior.

Smoothing filters can reduce the impact of fractional delays.

Performance of filters depends on fractional order characteristics.

Abstract

This paper analyzes a nuclear reactor power signal that suffers from network induced random delays in the shared data network while being fed-back to the Reactor Regulating System (RRS). A detailed study is carried out to investigate the self similarity of random delay dynamics due to the network traffic in shared medium. The fractionality or selfsimilarity in the network induced delay that corrupts the measured power signal coming from Self Powered Neutron Detectors (SPND) is estimated and analyzed. As any fractional order randomness is intrinsically different from conventional Gaussian kind of randomness, these delay dynamics need to be handled efficiently, before reaching the controller within the RRS. An attempt has been made to minimize the effect of the randomness in the reactor power transient data with few classes of smoothing filters. The performance measure of the smoothers with fractional order noise consideration is also investigated into.