Research output: Contribution to journal › Article › peer-review
Research output: Contribution to journal › Article › peer-review
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TY - JOUR
T1 - Data Mining Applied to Decision Support Systems for Power Transformers’ Health Diagnostics
AU - Khalyasmaa, Alexandra I.
AU - Matrenin, Pavel V.
AU - Eroshenko, Stanislav A.
AU - Manusov, Vadim Z.
AU - Bramm, Andrey M.
AU - Romanov, Alexey M.
PY - 2022/7
Y1 - 2022/7
N2 - This manuscript addresses the problem of technical state assessment of power transformers based on data preprocessing and machine learning. The initial dataset contains diagnostics results of the power transformers, which were collected from a variety of different data sources. It leads to dramatic degradation of the quality of the initial dataset, due to a substantial number of missing values. The problems of such real-life datasets are considered together with the performed efforts to find a balance between data quality and quantity. A data preprocessing method is proposed as a two-iteration data mining technology with simultaneous visualization of objects' observability in a form of an image of the dataset represented by a data area diagram. The visualization improves the decision-making quality in the course of the data preprocessing procedure. On the dataset collected by the authors, the two-iteration data preprocessing technology increased the dataset filling degree from 75% to 94%, thus the number of gaps that had to be filled in with the synthetic values was reduced by 2.5 times. The processed dataset was used to build machine-learning models for power transformers' technical state classification. A comparative analysis of different machine learning models was carried out. The outperforming efficiency of ensembles of decision trees was validated for the fleet of high-voltage power equipment taken under consideration. The resulting classification-quality metric, namely, F-1-score, was estimated to be 83%.
AB - This manuscript addresses the problem of technical state assessment of power transformers based on data preprocessing and machine learning. The initial dataset contains diagnostics results of the power transformers, which were collected from a variety of different data sources. It leads to dramatic degradation of the quality of the initial dataset, due to a substantial number of missing values. The problems of such real-life datasets are considered together with the performed efforts to find a balance between data quality and quantity. A data preprocessing method is proposed as a two-iteration data mining technology with simultaneous visualization of objects' observability in a form of an image of the dataset represented by a data area diagram. The visualization improves the decision-making quality in the course of the data preprocessing procedure. On the dataset collected by the authors, the two-iteration data preprocessing technology increased the dataset filling degree from 75% to 94%, thus the number of gaps that had to be filled in with the synthetic values was reduced by 2.5 times. The processed dataset was used to build machine-learning models for power transformers' technical state classification. A comparative analysis of different machine learning models was carried out. The outperforming efficiency of ensembles of decision trees was validated for the fleet of high-voltage power equipment taken under consideration. The resulting classification-quality metric, namely, F-1-score, was estimated to be 83%.
KW - data preprocessing
KW - equipment technical state
KW - feature engineering
KW - identification of technical condition
KW - machine learning applications
KW - power transformer
UR - https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=tsmetrics&SrcApp=tsm_test&DestApp=WOS_CPL&DestLinkType=FullRecord&KeyUT=000833110400001
UR - http://www.scopus.com/inward/record.url?scp=85136936305&partnerID=8YFLogxK
U2 - 10.3390/math10142486
DO - 10.3390/math10142486
M3 - Article
VL - 10
JO - Mathematics
JF - Mathematics
SN - 2227-7390
IS - 14
M1 - 2486
ER -
ID: 30720872