A Malleable Model Development for Critical Systems Supervision Integration

Luciano A. C. Lisboa; Thamiles R. Melo; Ikaro G. S. A. Campos; Matheus B. Aragão; Alexandre S. Ribeiro; Lucas C. Silva; Valéria L. da Silva; Antonio M. N. Lima; Alex A. B. Santos

doi:10.20944/preprints202406.0799.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 10 June 2024 / Approved: 11 June 2024 / Online: 12 June 2024 (08:11:40 CEST)

The critical systems, which the failure and malfunction may result in human, environment and financial severe damages, are essential components in various sectors and particularly in energy domains. Although unwished, integration error problems in supervision of critical systems occurs, incurring significant expenses because of operator’s subjective analysis and on hardware topology failures. In this work, a malleable model design approach is proposed to formulate and solve the integration error problem in the critical systems supervision in terms of reliability. A real Hybrid Power Plant (HPP) case is considered as a requirement for a study case with simulated data. A method framework with informal approach (C4 diagram) and formal approach (Hierarchical Coloured Petri Nets) in radial spectrum is applied to the HPP supervision design. Using formal methods, a formulation and solution to this problem through structured, scalable and compact mathematical representations are possible. This malleable model intends to guarantee functional correctness and then reliability of the plant supervision system based on system software architecture. Outcomes suggest corroboration that the malleable model is appropriate for 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.

Critical Systems; Automation System; Supervision; Integration; Reliability; Malleable Model; Formal Languages; Hybrid Power Plant

Engineering, Electrical and Electronic Engineering

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A Malleable Model Development for Critical Systems Supervision Integration

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