Assessment of the influence of information disorder on quality indicators of automated control systems.

Abstract

This study examines the impact of information disorder on quality indicators, specifically
economy and efficiency, in automated control systems (ACS) of power units of power plants. The
analysis focuses on power plant control systems as a complex entity consisting of informationally interconnected elements, adopting a macro approach. The effectiveness of automated control systems
is evaluated from the perspective of statistical physics. The research investigates the relationship
between information disorder and quality indicators, particularly the cost dependence of the control
system on the efficiency of the control complex. Findings reveal the importance of maintaining a
uniform level of automation at individual management levels to ensure consistent payback periods
and levels of irregularity. The study demonstrates the effectiveness of a hierarchical management
structure on an industry scale, where higher levels receive more generalized information in smaller
volumes. Furthermore, it establishes that the increase in information within the control system should
remain economically feasible and not exceed a certain level. Control algorithms should be
appropriately balanced, and a controlled amount of disorder (noise) within the complex is
economically advantageous, particularly in more complex management processes. Upon reaching the
upper economically justifiable level of automatic control and depletion of reserves, transitioning to a
new, more productive technology in ACS becomes highly relevant, offering increased efficiency.