Hidden Markov Model Based on Signals from Blocks of Semi-Markov System’s Elements and Its Application for Dynamics Analysis Energy Systems

Obzherin, Yuriy E.; Nikitin, M. M.; Sidorov, Stanislav M.

doi:10.1007/978-981-15-5580-0_39

Cited by 3 publications

(4 citation statements)

References 9 publications

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“…Consider the relationship between the states of the embedded Markov chain (EMC) of the enlarged model and signals, i.e. define the connection function 𝑅 𝑠|𝒙 [16,18]:…”

Section: Connection Of Model States With Signalsmentioning

confidence: 99%

“…Consider the problem of estimating the characteristics of a hidden Markov model, taking into account the introduced parameters, following [18,19].…”

Section: Evaluation Of Characteristics and Prediction Of Statesmentioning

confidence: 99%

“…In this paper, first, a semi-Markov model [8][9][10][11][12] of an autonomous WDC is constructed, which makes it possible to calculate the stationary and temporal characteristics of reliability. Then, following the methodology proposed by the authors [18], a hidden Markov model [15][16][17] is developed on its basis, which is used to solve forecasting problems and evaluate its characteristics, taking into account the given parameters.…”

Section: Introductionmentioning

confidence: 99%

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Hidden Markov Model and States Prediction of an Autonomous Wind-Diesel Complex

2023

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The problem of assessing the reliability and analyzing the functioning of an autonomous winddiesel complex, consisting of a wind power plant, working and standby diesel generators, an inverter and a storage battery, is considered. First, a semi-Markov model of an autonomous wind-diesel complex is built, which makes it possible to calculate the stationary and temporal reliability characteristics. Then, on its basis, a hidden Markov model is developed, which is used to solve the problems of predicting and evaluating its characteristics, taking into account the given parameters and the signal vector. The results of the study are obtained in a general form and are invariant with respect to the laws of distribution of random variables describing the elements of an autonomous wind-diesel complex. They allow you to simulate the functioning of the system under various distribution laws, based on statistical data, without modifying the model itself.

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“…Consider the relationship between the states of the embedded Markov chain (EMC) of the enlarged model and signals, i.e. define the connection function 𝑅 𝑠|𝒙 [16,18]:…”

Section: Connection Of Model States With Signalsmentioning

confidence: 99%

“…Consider the problem of estimating the characteristics of a hidden Markov model, taking into account the introduced parameters, following [18,19].…”

Section: Evaluation Of Characteristics and Prediction Of Statesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hidden Markov Model and States Prediction of an Autonomous Wind-Diesel Complex

2023

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“…Note that semi-Markov models, in contrast to Markov models, in addition to quantitative reliability characteristics, also allow us to determine the time characteristics. The theory of hidden Markov models [32][33] allows us to evaluate the dynamics of the system and predict its states based on the vector of signals received during its operation.…”

Section: Introductionmentioning

confidence: 99%

Distribution network section reliability analysis based on the semi-Markov model

Sidorov

Lugovkin

2022

IOP Conf. Ser.: Earth Environ. Sci.

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The digital training ground of Sevastopol State University (SevSU) is a physical model of a section of an intelligent distribution network of a new generation. In this article, a semi-Markov model of the distribution network section of the SevGU digital training ground is built. The reliability stationary characteristics of the considered section of the network are determined, two cases of consumer nutrition are considered. A numerical example of finding the reliability stationary characteristics using the obtained in the article formulas is given. The obtained results make it possible to analyze the reliability and efficiency of the distribution network sections.

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