Abstract
The paper presents the results of research on methods for constructing knowledge bases of decision support systems for dispatch control of power supply systems in crisis situations. Models of products based on Petri nets are proposed for assessing the parameters of reliability of power supply systems. An algorithm for calculating the reliability parameters of the electrical network using analytical matrix equations of Petri nets has been implemented. The work is devoted to the topical problem of intellectualization of power supply systems and means of automation of control of large power systems. The relevance of research in the field of building intelligent power systems is grounded. The basic requirements for the reliability of modern power supply systems are formulated. The concept of intellectualization of power systems and management of their reliability is a stable trend in the development of large energy systems and automated dispatch control systems. The solution to the problem lies in the implementation of intelligent decision support systems. The main task of the work is to model the reliability of the power supply system in order to build an effective knowledge base about the parameters of the reliability of the power supply system. For this purpose, we use a probabilistic approach to assessing reliability. This approach is based on the concepts of the probability of failure-free operation and the probability of failure, which are expressed in terms of the failure rate. On the basis of the obtained models, the construction of intelligent software systems for dispatching can be carried out taking into account the reliability indicators of the components of power supply systems. As a mechanism for formalizing failure circuits in power supply circuits, the production approach of knowledge representation is applied, which implements conjunctive-disjunctive connections of statements associated with failures. The outlined approach is invariant in relation to subject areas, which makes it possible to build effective decision support systems in the field of operational emergency control of the energy system.
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