Artikel
Modifications of probabilistic models of states evolution for reliability analysis of district heating systems
The analysis and synthesis of reliability of energy systems, particularly district heating systems (DHS), is based on probabilistic modeling of different system states and events connecting these states (usually, these are failures and restoration of system components). The paper deals with the problem of modeling the probabilities of DHS states based on a Markov random process under conditions that can be characteristic for actual systems: joint and dependent failures of several components. For this purpose, it probabilistic models of the evolution of events in DHS taking into account non-ordinary events and dependent between them are developed. The methodology is based on the theory of Markov random processes and the basic laws of probability theory. The formation principles of developed models are illustrated by the state graphs. Numerical studies were carried out, the results of which allows determine some properties and application limits of developed models.
- Language
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Englisch
- Bibliographic citation
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Journal: Energy Reports ; ISSN: 2352-4847 ; Volume: 6 ; Year: 2020 ; Issue: 2 ; Pages: 293-298 ; Amsterdam: Elsevier
- Classification
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Wirtschaft
- Subject
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Availability factor
District heating system
Failure and restoration rates
Markov random processes
Non-ordinary and depending events
Probabilistic models
Rate of transition
Reliability analysis
Reliability indices
States evolution
- Event
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Geistige Schöpfung
- (who)
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Postnikov, Ivan
Stennikov, Valery
- Event
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Veröffentlichung
- (who)
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Elsevier
- (where)
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Amsterdam
- (when)
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2020
- DOI
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doi:10.1016/j.egyr.2019.11.077
- Handle
- Last update
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10.03.2025, 11:41 AM CET
Data provider
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ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Artikel
Associated
- Postnikov, Ivan
- Stennikov, Valery
- Elsevier
Time of origin
- 2020
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