title: ÐÐµÑÐ¾Ð´Ñ Ð¸ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ñ Ð¿ÑÐµÐ´Ð¸ÐºÑÐ¸Ð²Ð½Ð¾Ð¹ Ð°Ð½Ð°Ð»Ð¸ÑÐ¸ÐºÐ¸ ÐºÐ¾Ð¼Ð¿Ð¾Ð½ÐµÐ½ÑÐ¾Ð² ÑÐ¸ÑÑÐ¾Ð²Ð¾Ð¹ Ð¿Ð¾Ð´ÑÑÐ°Ð½ÑÐ¸Ð¸
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Methods and algorithms for predictive analytics of digital substation components
Creators: name: ÐÑÐ¾ÐºÐ¾ÑÑÐµÐ², ÐÐ¸ÐºÑÐ¾Ñ
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2024
Subjects: Ð¢ÑÐ°Ð½ÑÑÐ¾ÑÐ¼Ð°ÑÐ¾ÑÑ
ÐÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
Ð­Ð»ÐµÐºÑÑÐ¸ÑÐµÑÐºÐ¸Ðµ Ð¿Ð¾Ð´ÑÑÐ°Ð½ÑÐ¸Ð¸
Ð­Ð½ÐµÑÐ³ÐµÑÐ¸ÐºÐ°
ÑÐ¸ÑÑÐ¾Ð²Ð°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ
ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ
Ð¿ÑÐµÐ´Ð¸ÐºÑÐ¸Ð²Ð½Ð°Ñ Ð°Ð½Ð°Ð»Ð¸ÑÐ¸ÐºÐ°
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Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑÑÐ°Ð»ÑÐ½Ð°Ñ Ð°Ð½Ð°Ð»Ð¸ÑÐ¸ÐºÐ°
digital model
classification
predictive analytics
data analysis
intelligent analytics

description: Ð ÑÐ°Ð±Ð¾ÑÐµ Ð¸ÑÑÐ»ÐµÐ´ÑÐµÑÑÑ Ð²Ð¾Ð·Ð¼Ð¾Ð¶Ð½Ð¾ÑÑÐ¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð´Ð°Ð½Ð½ÑÑ, Ð¿Ð¾Ð»ÑÑÐµÐ½Ð½ÑÑ Ð¿ÑÐ¸ Ð¿Ð¾Ð¼Ð¾ÑÐ¸ ÑÐ¸ÑÑÐ¾Ð²Ð¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸ ÑÑÐ°Ð½ÑÑÐ¾ÑÐ¼Ð°ÑÐ¾ÑÐ½Ð¾Ð¹ Ð»Ð¸Ð½Ð¸Ð¸ Ð¿Ð¾Ð´ÑÑÐ°Ð½ÑÐ¸Ð¸, Ð° ÑÐ°ÐºÐ¶Ðµ Ð²Ð¾Ð·Ð¼Ð¾Ð¶Ð½Ð¾ÑÑÑ Ð¿ÑÐµÐ´ÑÐºÐ°Ð·ÑÐ²Ð°ÑÑ Ð²Ð¾Ð·Ð½Ð¸ÐºÐ½Ð¾Ð²ÐµÐ½Ð¸Ðµ Ð°Ð²Ð°ÑÐ¸Ð¹Ð½ÑÑ ÑÐ¸ÑÑÐ°ÑÐ¸Ð¹ Ð¿ÑÐ¸ Ð¿Ð¾Ð¼Ð¾ÑÐ¸ Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐµÐ²ÑÑ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹. Ð ÑÐ°Ð±Ð¾ÑÐµ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÑÑÑÑ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÐµ Ð½Ð°Ð±Ð¾ÑÑ Ð´Ð°Ð½Ð½ÑÑ, Ð¾ÑÐ»Ð¸ÑÐ°ÑÑÐ¸ÐµÑÑ ÐºÐ°Ðº Ð¿ÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½Ð½ÑÐ¼Ð¸ Ð² Ð½Ð¸Ñ ÑÐµÐ¶Ð¸Ð¼Ð°Ð¼Ð¸ ÑÐ°Ð±Ð¾ÑÑ ÑÑÐ°Ð½ÑÑÐ¾ÑÐ¼Ð°ÑÐ¾ÑÐ½Ð¾Ð¹ Ð»Ð¸Ð½Ð¸Ð¸, ÑÐ°Ðº Ð¸ ÑÑÐµÐ¿ÐµÐ½ÑÑ Ð¿ÑÐ¸Ð±Ð»Ð¸Ð¶ÐµÐ½Ð½Ð¾ÑÑÐ¸ Ð¼Ð¾Ð´ÐµÐ»Ð¸ÑÑÐµÐ¼ÑÑ ÑÐ°ÑÐ°ÐºÑÐµÑÐ¸ÑÑÐ¸Ðº Ðº ÑÐµÐ°Ð»ÑÐ½ÑÐ¼ ÑÑÐ»Ð¾Ð²Ð¸ÑÐ¼. ÐÐ°Ð»ÐµÐµ ÑÐ°ÑÑÐ¼Ð°ÑÑÐ¸Ð²Ð°ÐµÑÑÑ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ Ð¿ÑÐ¸Ð²ÐµÐ´ÐµÐ½Ð¸Ñ Ð´Ð°Ð½Ð½ÑÑ Ðº Ð´Ð¾ÑÑÑÐ¿Ð½Ð¾Ð¼Ñ Ð´Ð»Ñ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐ¸ Ð²Ð¸Ð´Ñ Ð¸ Ð²Ð¾Ð·Ð¼Ð¾Ð¶Ð½ÑÐµ ÑÐ°ÑÑÐ¸ÑÐµÐ½Ð¸Ñ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ¸ Ð¸ ÑÑÑÑÐºÑÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð´Ð°Ð½Ð½ÑÑ. Ð ÑÐ°ÑÑÐ½Ð¾ÑÑÐ¸ - Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ Ð½Ð¸Ð·ÐºÐ¾ÑÐ°ÑÑÐ¾ÑÐ½ÑÑ ÑÐ¸Ð»ÑÑÑÐ¾Ð² Ð´Ð»Ñ ÑÐ¼ÐµÐ½ÑÑÐµÐ½Ð¸Ñ Ð·Ð°ÑÑÐ¼Ð»ÐµÐ½Ð½Ð¾ÑÑÐ¸ Ð´Ð°Ð½Ð½ÑÑ Ð¸ ÑÐ¾ÑÑÐ°Ð²Ð»ÐµÐ½Ð¸Ðµ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÑ Ð¸ÐµÑÐ°ÑÑÐ¸ÑÐµÑÐºÐ¸Ñ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ ÑÐ»Ð¾Ð¶Ð½ÑÑ ÑÐ¸ÑÑÐµÐ¼. Ð ÑÐ¾Ð´Ðµ Ð¾Ð¿ÑÑÐ¾Ð² ÑÐ¾ÑÑÐ°Ð²Ð»ÑÐµÑÑÑ Ð¸ Ð½Ð°ÑÑÑÐ°Ð¸Ð²Ð°ÐµÑÑÑ Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐµÐ²Ð¾Ð¹ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¾Ñ, Ð¿Ð¾Ð·Ð²Ð¾Ð»ÑÑÑÐ¸Ð¹ Ð´Ð¾Ð±Ð¸ÑÑÑÑ 96% ÑÐ¾ÑÐ½Ð¾ÑÑÐ¸ Ð² Ð·Ð°Ð´Ð°ÑÐµ Ð¼Ð½Ð¾Ð³Ð¾ÐºÐ°ÑÐµÐ³Ð¾ÑÐ¸Ð°Ð»ÑÐ½Ð¾Ð¹ Ð¾ÑÐµÐ½ÐºÐ¸ ÑÐ°ÑÑÐ¼Ð°ÑÑÐ¸Ð²Ð°ÐµÐ¼ÑÑ Ð´Ð°Ð½Ð½ÑÑ, Ð° ÑÐ°ÐºÐ¶Ðµ Ð¿ÑÐµÐ´Ð¸ÐºÑÐ¸Ð²Ð½Ð°Ñ Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÑ, Ð¿ÑÐµÐ´ÑÐºÐ°Ð·ÑÐ²Ð°ÑÑÐ°Ñ Ð¿Ð¾Ð²ÐµÐ´ÐµÐ½Ð¸Ðµ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ñ ÑÐ¾ÑÐ½Ð¾ÑÑÑÑ 90% Ð¿ÑÐ¸ Ð·Ð°Ð´Ð°Ð½Ð½Ð¾Ð¼ ÑÐ¸ÑÐ»Ðµ Ð´Ð¾ÑÑÑÐ¿Ð½ÑÑ Ð¿ÑÐµÐ´ÑÐ´ÑÑÐ¸Ñ ÑÐ¾ÑÑÐ¾ÑÐ½Ð¸Ð¹.
description: This thesis investigates the possibility of classifying the data obtained using a digital model of a substation transformer line, as well as the possibility of predicting the occurrence of emergency situations using neural network models. Different data sets are used in this work, differing both in the transformer line operating modes represented in them and in the degree of closeness of the modeled characteristics to real conditions. The algorithm of data reduction to a form accessible for neural network training and possible extensions of data processing and structuring methods are discussed further. In particular, the application of low-pass filters to reduce data noise and the compilation of various hierarchical models of complex systems. In the course of experiments, a neural network classifier, which allows to achieve 96% accuracy in the task of multi-category evaluation of the data under consideration, as well as a predictive neural network, which predicts the model behavior with 90% accuracy at a given number of available previous states, are compiled and tuned.