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A Fuzzy-Logic Approach for Health Index Estimation of OLTCs in Power Transformers.

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Title: A Fuzzy-Logic Approach for Health Index Estimation of OLTCs in Power Transformers.
Authors: Rokani, Vasiliki1 (AUTHOR) cpsomop@uniwa.gr, Kaminaris, Stavros D. (AUTHOR), Psomopoulos, C. S.1 (AUTHOR), Karaisas, Petros1 (AUTHOR), Menti, Anthoula1 (AUTHOR)
Source: Energies (19961073). Sep2026, Vol. 19 Issue 18, p4378. 31p.
Subject Terms: *Fuzzy logic, *Condition-based maintenance, *Statistical reliability, *Fault diagnosis, *Power transformers, *Electric transformers
Abstract: Power transformers are critical assets in complex power grid systems, yet On-Load Tap Changers (OLTCs) account for over 30% of documented outages. This study introduces a Health Index (HI) model for OLTCs that employs a Fuzzy-Logic system to enhance condition-based maintenance (CBM) techniques. This research presents a component-wise, Fuzzy-Logic–based HI evaluation using the Scoring Methodology. The OLTC component is subdivided into six smaller components or contributors. Every contributor is a crucial subsystem whose efficacy is vital to the transformer's overall reliability. Each contributor is divided into three sub-contributors/values and assessed using a three-tier classification scale (A, B, or C). These values could indicate condition measurements, operational observations, and diagnostic data. Each value is evaluated against reference ranges or boundary values derived from a synthesis of international standards, statistical population studies, and expert knowledge. To verify the precision of the proposed methodology, several defective OLTC cases were evaluated under diverse operational settings. This Fuzzy-Logic (FL) approach seeks to deliver a more accurate and interpretable assessment of OLTC condition than traditional crisp-value methods by integrating expert knowledge, diagnostic metrics, and standards within a structured Fuzzy-Inference framework. The implicit FL model is inherently more attuned to early indicators of deterioration and hidden risk factors that may be underestimated in conventional expert evaluations. Implementation of this intelligent monitoring approach enables early fault diagnosis, extends the transformer's operational life, and reduces the risk of catastrophic failures and unplanned outages in the electrical power network. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 197391745
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Label: Title
  Group: Ti
  Data: A Fuzzy-Logic Approach for Health Index Estimation of OLTCs in Power Transformers.
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  Data: <searchLink fieldCode="AR" term="%22Rokani%2C+Vasiliki%22">Rokani, Vasiliki</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> cpsomop@uniwa.gr</i><br /><searchLink fieldCode="AR" term="%22Kaminaris%2C+Stavros+D%2E%22">Kaminaris, Stavros D.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Psomopoulos%2C+C%2E+S%2E%22">Psomopoulos, C. S.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Karaisas%2C+Petros%22">Karaisas, Petros</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Menti%2C+Anthoula%22">Menti, Anthoula</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Sep2026, Vol. 19 Issue 18, p4378. 31p.
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  Data: *<searchLink fieldCode="DE" term="%22Fuzzy+logic%22">Fuzzy logic</searchLink><br />*<searchLink fieldCode="DE" term="%22Condition-based+maintenance%22">Condition-based maintenance</searchLink><br />*<searchLink fieldCode="DE" term="%22Statistical+reliability%22">Statistical reliability</searchLink><br />*<searchLink fieldCode="DE" term="%22Fault+diagnosis%22">Fault diagnosis</searchLink><br />*<searchLink fieldCode="DE" term="%22Power+transformers%22">Power transformers</searchLink><br />*<searchLink fieldCode="DE" term="%22Electric+transformers%22">Electric transformers</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Power transformers are critical assets in complex power grid systems, yet On-Load Tap Changers (OLTCs) account for over 30% of documented outages. This study introduces a Health Index (HI) model for OLTCs that employs a Fuzzy-Logic system to enhance condition-based maintenance (CBM) techniques. This research presents a component-wise, Fuzzy-Logic–based HI evaluation using the Scoring Methodology. The OLTC component is subdivided into six smaller components or contributors. Every contributor is a crucial subsystem whose efficacy is vital to the transformer's overall reliability. Each contributor is divided into three sub-contributors/values and assessed using a three-tier classification scale (A, B, or C). These values could indicate condition measurements, operational observations, and diagnostic data. Each value is evaluated against reference ranges or boundary values derived from a synthesis of international standards, statistical population studies, and expert knowledge. To verify the precision of the proposed methodology, several defective OLTC cases were evaluated under diverse operational settings. This Fuzzy-Logic (FL) approach seeks to deliver a more accurate and interpretable assessment of OLTC condition than traditional crisp-value methods by integrating expert knowledge, diagnostic metrics, and standards within a structured Fuzzy-Inference framework. The implicit FL model is inherently more attuned to early indicators of deterioration and hidden risk factors that may be underestimated in conventional expert evaluations. Implementation of this intelligent monitoring approach enables early fault diagnosis, extends the transformer's operational life, and reduces the risk of catastrophic failures and unplanned outages in the electrical power network. [ABSTRACT FROM AUTHOR]
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        Value: 10.3390/en19184378
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        Text: English
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        PageCount: 31
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      – SubjectFull: Statistical reliability
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      – SubjectFull: Fault diagnosis
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      – SubjectFull: Power transformers
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      – SubjectFull: Electric transformers
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              M: 09
              Text: Sep2026
              Type: published
              Y: 2026
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