Power Transformer Condition-Based Evaluation and Maintenance (CBM) Using Dempster–Shafer Theory (DST)

Transformers are the most important elements in the power system. Due to their mass and complexity, they require constant monitoring and maintenance. Maintenance of power transformers increases the availability of the power system. The large number of substations and the specifics of their locations...

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Main Authors: Damir Blažević, Tomislav Keser, Hrvoje Glavaš, Robert Noskov
Format: Article
Language:English
Published: MDPI AG 2023-05-01
Series:Applied Sciences
Subjects:
Online Access:https://www.mdpi.com/2076-3417/13/11/6731
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author Damir Blažević
Tomislav Keser
Hrvoje Glavaš
Robert Noskov
author_facet Damir Blažević
Tomislav Keser
Hrvoje Glavaš
Robert Noskov
author_sort Damir Blažević
collection DOAJ
description Transformers are the most important elements in the power system. Due to their mass and complexity, they require constant monitoring and maintenance. Maintenance of power transformers increases the availability of the power system. The large number of substations and the specifics of their locations make condition-based maintenance (CBM) useful as part of the system’s on-demand response. Unlike other system responses, the transformer contains a large amount of uncertain information, both qualitative and numerical. A large amount of information is necessary to implement CBM, but due to the often incomplete information, an analysis tool is essential. In this paper, a multi-level condition assessment framework based on evidential reasoning is proposed. A model for condition-based maintenance of a power transformer and procedures for the aggregation process based on evidential reasoning are presented. The implementation of the decomposition model with appropriate weights of a baseline and general attributes was made. Based on the decomposition model, the data and ratings of baseline attributes were collected. By carrying out the aggregation process, the ratings of the baseline attributes, as well as the ratings of the condition of the individual elements and the overall rating of the system condition as a whole, for several points in time, were obtained. The scientific contribution of the work is the proposal of an analysis that provides an insight into the condition of a complex technical system based on a single numerical value, thus determining its priority in the maintenance process.
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spelling doaj.art-c3629a53b7604d0e99f5ec9539c2f24d2023-11-18T07:35:59ZengMDPI AGApplied Sciences2076-34172023-05-011311673110.3390/app13116731Power Transformer Condition-Based Evaluation and Maintenance (CBM) Using Dempster–Shafer Theory (DST)Damir Blažević0Tomislav Keser1Hrvoje Glavaš2Robert Noskov3Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Josip Juraj Strossmayer University in Osijek, Kneza Trpimira 2B, 31000 Osijek, CroatiaFaculty of Electrical Engineering, Computer Science and Information Technology Osijek, Josip Juraj Strossmayer University in Osijek, Kneza Trpimira 2B, 31000 Osijek, CroatiaFaculty of Electrical Engineering, Computer Science and Information Technology Osijek, Josip Juraj Strossmayer University in Osijek, Kneza Trpimira 2B, 31000 Osijek, CroatiaCroatian Transmission System Operator Ltd., Kupska 4, 10000 Zagreb, CroatiaTransformers are the most important elements in the power system. Due to their mass and complexity, they require constant monitoring and maintenance. Maintenance of power transformers increases the availability of the power system. The large number of substations and the specifics of their locations make condition-based maintenance (CBM) useful as part of the system’s on-demand response. Unlike other system responses, the transformer contains a large amount of uncertain information, both qualitative and numerical. A large amount of information is necessary to implement CBM, but due to the often incomplete information, an analysis tool is essential. In this paper, a multi-level condition assessment framework based on evidential reasoning is proposed. A model for condition-based maintenance of a power transformer and procedures for the aggregation process based on evidential reasoning are presented. The implementation of the decomposition model with appropriate weights of a baseline and general attributes was made. Based on the decomposition model, the data and ratings of baseline attributes were collected. By carrying out the aggregation process, the ratings of the baseline attributes, as well as the ratings of the condition of the individual elements and the overall rating of the system condition as a whole, for several points in time, were obtained. The scientific contribution of the work is the proposal of an analysis that provides an insight into the condition of a complex technical system based on a single numerical value, thus determining its priority in the maintenance process.https://www.mdpi.com/2076-3417/13/11/6731condition-based maintenancepower transformerevidential reasoningquantitative and qualitative data analysis
spellingShingle Damir Blažević
Tomislav Keser
Hrvoje Glavaš
Robert Noskov
Power Transformer Condition-Based Evaluation and Maintenance (CBM) Using Dempster–Shafer Theory (DST)
Applied Sciences
condition-based maintenance
power transformer
evidential reasoning
quantitative and qualitative data analysis
title Power Transformer Condition-Based Evaluation and Maintenance (CBM) Using Dempster–Shafer Theory (DST)
title_full Power Transformer Condition-Based Evaluation and Maintenance (CBM) Using Dempster–Shafer Theory (DST)
title_fullStr Power Transformer Condition-Based Evaluation and Maintenance (CBM) Using Dempster–Shafer Theory (DST)
title_full_unstemmed Power Transformer Condition-Based Evaluation and Maintenance (CBM) Using Dempster–Shafer Theory (DST)
title_short Power Transformer Condition-Based Evaluation and Maintenance (CBM) Using Dempster–Shafer Theory (DST)
title_sort power transformer condition based evaluation and maintenance cbm using dempster shafer theory dst
topic condition-based maintenance
power transformer
evidential reasoning
quantitative and qualitative data analysis
url https://www.mdpi.com/2076-3417/13/11/6731
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