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Development of a Multiple Regression Model for Early Diagnosis of Transformer Oil Condition

  • Research Article-Electrical Engineering
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Abstract

For early diagnosis of transformer oils condition according to a set of diagnostic attributes a model of multiple regression has been developed. A distinctive feature of the proposed model is that the values of the operating life is a function of the values of the oil indicators, and the decision about the condition of the oils is made on the basis of comparison of the real operating life with the operating life calculated by regression model. In order to take into account the peculiarities of oil ageing under conditions of long-term operation a complex analysis of stochastic correlation between the quality indicators of transformer oils has been carried out. The procedure of statistical processing of the results of operational tests is proposed, which allows filtering out the test results that contain gross errors and omissions. A multiple regression model is trained taking into account transformer loading and its adequacy is checked. Examples of practical use of the developed model for operating transformers are given. Practical use of the developed model allows detecting transformers with accelerated oil ageing at an early stage when oil indicators are in the range of permissible values. This allows the substation operating personnel to inhibit the oil ageing processes in time, which, in turn, makes it possible to extend the life of not only the liquid but also the main insulation of transformers.

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  1. Department of Electric Power Transmission, National Technical University “Kharkiv Polytechnic Institute”, 2, Kyrpychova str., Kharkiv, 61002, Ukraine

    Oleg Shutenko & Serhii Ponomarenko

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  1. Oleg Shutenko
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  2. Serhii Ponomarenko
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Correspondence to Serhii Ponomarenko.

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Shutenko, O., Ponomarenko, S. Development of a Multiple Regression Model for Early Diagnosis of Transformer Oil Condition. Arab J Sci Eng 47, 14119–14132 (2022). https://doi.org/10.1007/s13369-021-06418-5

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  • DOI: https://doi.org/10.1007/s13369-021-06418-5

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