Leading prospects for the development of production asset management systems of Russian thermal power plants

Okley, Pavel

doi:10.2991/icseal-19.2019.56

Cited by 2 publications

(2 citation statements)

References 15 publications

(17 reference statements)

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“…At the subsequent stages of the formation of a digital model, calendar and resource planning of works and tracking of their implementation are carried out. It is possible to use three approaches to building digital models of TPPs asset management [9,10]:…”

Section: The Economic Effect Of Owning An Assetmentioning

confidence: 99%

Application of Methods of Balance Analysis for the Development of Digital Models for Managing the Production Assets of TPP

Lisin¹,

Parshina²,

Vasilieva³

2021

Proceedings of the 1st International Scientific Conference "Legal Regulation of the Digital Economy and Digital Relations: Prob

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The article examines the possibility of using the methods of balance analysis for the development of digital models for managing the production assets of thermal power plants. It is shown that the balance method is suitable for compiling universal input-output matrices, which can be used for a mathematical description of the expenditure of financial and material resources in the production process of a power plant. An economic and mathematical model of the production assets management system in the form of matrix equations of price, added value, output and supply of energy resources and products is proposed. The developed model provides dual links between economic and technological factors of production and allows organizing the management of production assets of a power plant directly according to the economic effect of their use.

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Section: The Economic Effect Of Owning An Assetmentioning

confidence: 99%

Application of Methods of Balance Analysis for the Development of Digital Models for Managing the Production Assets of TPP

Lisin¹,

Parshina²,

Vasilieva³

2021

Proceedings of the 1st International Scientific Conference "Legal Regulation of the Digital Economy and Digital Relations: Prob

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“…In recent years, the concept of managing production assets based on the technical condition of equipment used at energy enterprises has grown into a system for organizing the activities of an energy enterprise as a whole [8][9][10]. Thus, in order to solve the problem of planning maintenance and repairs at power plants, it is merely not enough to assess the current technical conditions of the equipment.…”

Section: Introductionmentioning

confidence: 99%

Power Equipment Defects Prediction Based on the Joint Solution of Classification and Regression Problems Using Machine Learning Methods

et al. 2021

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Our paper proposes a method for constructing a system for predicting defects and failures of power equipment and the time of their occurrence based on the joint solution of regression and classification problems using machine learning methods. A distinctive feature of this method is the use of the equipment’s technical condition index as an informative parameter. The results of calculating and visualizing the technical condition index in relation to the electro-hydraulic automatic control system of hydropower turbine when predicting the defect “clogging of drainage channels” showed that its determination both for an equipment and for a group of its functional units allows one to quickly and with the required accuracy assess the arising technological disturbances in the operation of power equipment. In order to predict the behavior of the technical condition index of the automatic control system of the turbine, the optimal tuning of the LSTM model of the recurrent neural network was developed and carried out. The result of the application of the model was the forecast of the technical condition index achievement and the limiting characteristic according to the current time data on its values. The developed model accurately predicted the behavior of the technical condition index at time intervals of 3 and 10 h, which made it possible to draw a conclusion about its applicability for early identification of the investigated defect in the automatic control system of the turbine. Thus, we can conclude that the joint solution of regression and classification problems using an information parameter in the form of a technical condition index allows one to develop systems for predicting defects, one significant advantage of which is the ability to early determine the development of degradation phenomena in power equipment.

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Leading prospects for the development of production asset management systems of Russian thermal power plants

Cited by 2 publications

References 15 publications

Application of Methods of Balance Analysis for the Development of Digital Models for Managing the Production Assets of TPP

Application of Methods of Balance Analysis for the Development of Digital Models for Managing the Production Assets of TPP

Power Equipment Defects Prediction Based on the Joint Solution of Classification and Regression Problems Using Machine Learning Methods

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