In this paper, we review innovative approaches to assess the technical condition of
autotransformers using mathematical methods. The main purpose of these methods is to increase the
efficiency of diagnostics and prevent possible failures in power systems. The developed mathematical
expression is based on complex data analysis, application of statistical methods and machine learning
technologies.
This makes it possible to achieve more accurate and earlier detection of potential problems. As
a result of these innovative approaches, the possibilities for early detection and prevention of faults are
significantly increased. This is important for ensuring the smooth operation of power systems and
preventing potential accidents. Thus, the use of mathematical methods in the assessment of the technical
condition of autotransformers is an effective and promising direction of development. It allows to
increase reliability and safety of power systems operation, as well as to reduce the risks of unforeseen
failures.
In this paper, we review innovative approaches to assess the technical condition of
autotransformers using mathematical methods. The main purpose of these methods is to increase the
efficiency of diagnostics and prevent possible failures in power systems. The developed mathematical
expression is based on complex data analysis, application of statistical methods and machine learning
technologies.
This makes it possible to achieve more accurate and earlier detection of potential problems. As
a result of these innovative approaches, the possibilities for early detection and prevention of faults are
significantly increased. This is important for ensuring the smooth operation of power systems and
preventing potential accidents. Thus, the use of mathematical methods in the assessment of the technical
condition of autotransformers is an effective and promising direction of development. It allows to
increase reliability and safety of power systems operation, as well as to reduce the risks of unforeseen
failures.
| № | Muallifning F.I.Sh. | Lavozimi | Tashkilot nomi |
|---|---|---|---|
| 1 | Pirmatov .B. | DSc, Professor | Tashkent state technical university named after Islom Karimov |
| 2 | Abdullabekova .R. | Doctoral student | Tashkent State Technical University |
| № | Havola nomi |
|---|---|
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