The Development of a Malleable Model for Critical System Supervision Integration

Abstract

Critical systems, in which failure and malfunction may result in severe human, environmental, and financial damages, are essential components in various sectors and particularly in energy domains. Although undesirable, integration error problems in the supervision of critical systems do occur, incurring significant expenses due to an operator’s subjective analysis and hardware topology failures. In this work, a malleable model design approach is proposed to formulate and solve the integration error problem in critical systems’ supervision in terms of reliability. A real hybrid power plant (HPP) case is considered for a case study with simulated data. A method framework with an informal approach (C4 diagram) and formal approach (hierarchical colored Petri nets) in a radial spectrum is applied to the HPP supervision design. In using formal methods, a formulation and solution to this problem through structured, scalable, and compact mathematical representations are possible. This malleable model is intended to guarantee the functional correctness and also reliability of the plant supervision system based on system software architecture. The outcomes suggest that the malleable model is appropriate for the energy domain and can be used for other types of critical systems, bringing all the benefits of this methodology to the context in which it will be applied.