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Article title ASSESSMENT AND FORECASTING THE INSULATION OF POWER CABLE LINES
Authors M.N. Dubyago, V.Kh. Pshikhopov, N.K. Poluyanovich
Section SECTION V. AUTOMATION AND CONTROL
Month, Year 07, 2015 @en
Index UDC 621.315.3: 621.316.99
DOI
Abstract Relevance of the work lies in the fact that the reliability and continuity of power cable lines (PCL) is strongly dependent on the temperature of the line and the external environment. This is particularly evident in the operation in the climatic conditions of the country in which the thermal insulation properties vary significantly depending on the season. In operation, the heating occurs under load due to the PCL active power losses in the conductor and the insulation element. If the core temperature approaches the maximum, then begins the process of intensive thermal wear, aging and its thermal insulation breakdown. This indicator reliability has been the subject of study in this work. Resource electrical insulation of the cable determines the actual operating time and lifespan characterizes the calendar time from the date of putting into operation regardless of operating time and load factor. Thus, in the article the problem of assessment and prediction of residual life of cable systems of power supply, in conditions of thermal modes of operation. To achieve the goal the following objectives: – to establish the effect of temperature of the line, external factors and operational modes on the insulating properties of the PCL; – on the basis of a mathematical model of aging and identified changes in the properties of isolation from operational factors suggest a methodology to assess resource PCL. It is shown that the evaluation of the used and residual life of the insulation can be obtained from measurements of the insulation, depending on the operating parameters using a mathematical model of aging. We obtained and constructed according to the function of the aging time and temperature, built at various parameters of the Weibull distribution.

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Keywords Insulating materials; resources; non-invasive diagnosis; forecasting.
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