A New Transformer FRA Measurement Technique to Reach Smart Interpretation for Inter-Disk Faults

Venera Nurmanova, Mehdi Bagheri, Amin Zollanvari, Kamilla Aliakhmet, Yerbol Akhmetov, Gevork B. Gharehpetian

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Transformers are utilized in generation, transmission, and distribution power system network, and face an enormous number of hazards during their course of operation. Frequency response analysis (FRA) is an inexpensive, accurate, and non-destructive technique to explore the transformer mechanical integrity very fast. However, FRA results interpretation is not being automated yet. This study introduces a new setup for FRA measurement that can assist to leave the conventional FRA data interpretation techniques and obtain smart interpretation. Hence, FRA setups and interpretation techniques are studied and formulated in this paper. A new measurement technique is introduced and discussed in detail. Practical studies are performed over distribution and power transformers and FRA data are recorded for inter-disk fault. The analysis of fault severity, which is obatined in this paper, is an advantage of the proposed measurement technique. In this regard, the techniques from machine learning and numerical analysis are employed to train a predictive engine for smart interpretation of FRA data. It is revealed that the proposed intelligent technique is capable of interpreting, detecting, and classifying the transformer winding inter-disk fault and its severity. The new introduced FRA measurement setup is also able to support the online FRA data assessment.

Original languageEnglish
Article number8682123
Pages (from-to)1508-1519
Number of pages12
JournalIEEE Transactions on Power Delivery
Volume34
Issue number4
DOIs
Publication statusPublished - Aug 1 2019

Fingerprint

Frequency response
Transformer windings
Power transformers
Learning systems
Numerical analysis
Hazards
Engines

Keywords

  • FRA interpretation
  • Frequency response analysis (FRA)
  • online monitoring
  • short-circuit detection

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

Cite this

A New Transformer FRA Measurement Technique to Reach Smart Interpretation for Inter-Disk Faults. / Nurmanova, Venera; Bagheri, Mehdi; Zollanvari, Amin; Aliakhmet, Kamilla; Akhmetov, Yerbol; Gharehpetian, Gevork B.

In: IEEE Transactions on Power Delivery, Vol. 34, No. 4, 8682123, 01.08.2019, p. 1508-1519.

Research output: Contribution to journalArticle

Nurmanova, Venera ; Bagheri, Mehdi ; Zollanvari, Amin ; Aliakhmet, Kamilla ; Akhmetov, Yerbol ; Gharehpetian, Gevork B. / A New Transformer FRA Measurement Technique to Reach Smart Interpretation for Inter-Disk Faults. In: IEEE Transactions on Power Delivery. 2019 ; Vol. 34, No. 4. pp. 1508-1519.
@article{63ce3c2c7773482289d70c37133c16d1,
title = "A New Transformer FRA Measurement Technique to Reach Smart Interpretation for Inter-Disk Faults",
abstract = "Transformers are utilized in generation, transmission, and distribution power system network, and face an enormous number of hazards during their course of operation. Frequency response analysis (FRA) is an inexpensive, accurate, and non-destructive technique to explore the transformer mechanical integrity very fast. However, FRA results interpretation is not being automated yet. This study introduces a new setup for FRA measurement that can assist to leave the conventional FRA data interpretation techniques and obtain smart interpretation. Hence, FRA setups and interpretation techniques are studied and formulated in this paper. A new measurement technique is introduced and discussed in detail. Practical studies are performed over distribution and power transformers and FRA data are recorded for inter-disk fault. The analysis of fault severity, which is obatined in this paper, is an advantage of the proposed measurement technique. In this regard, the techniques from machine learning and numerical analysis are employed to train a predictive engine for smart interpretation of FRA data. It is revealed that the proposed intelligent technique is capable of interpreting, detecting, and classifying the transformer winding inter-disk fault and its severity. The new introduced FRA measurement setup is also able to support the online FRA data assessment.",
keywords = "FRA interpretation, Frequency response analysis (FRA), online monitoring, short-circuit detection",
author = "Venera Nurmanova and Mehdi Bagheri and Amin Zollanvari and Kamilla Aliakhmet and Yerbol Akhmetov and Gharehpetian, {Gevork B.}",
year = "2019",
month = "8",
day = "1",
doi = "10.1109/TPWRD.2019.2909144",
language = "English",
volume = "34",
pages = "1508--1519",
journal = "IEEE Transactions on Power Delivery",
issn = "0885-8977",
publisher = "IEEE",
number = "4",

}

TY - JOUR

T1 - A New Transformer FRA Measurement Technique to Reach Smart Interpretation for Inter-Disk Faults

AU - Nurmanova, Venera

AU - Bagheri, Mehdi

AU - Zollanvari, Amin

AU - Aliakhmet, Kamilla

AU - Akhmetov, Yerbol

AU - Gharehpetian, Gevork B.

PY - 2019/8/1

Y1 - 2019/8/1

N2 - Transformers are utilized in generation, transmission, and distribution power system network, and face an enormous number of hazards during their course of operation. Frequency response analysis (FRA) is an inexpensive, accurate, and non-destructive technique to explore the transformer mechanical integrity very fast. However, FRA results interpretation is not being automated yet. This study introduces a new setup for FRA measurement that can assist to leave the conventional FRA data interpretation techniques and obtain smart interpretation. Hence, FRA setups and interpretation techniques are studied and formulated in this paper. A new measurement technique is introduced and discussed in detail. Practical studies are performed over distribution and power transformers and FRA data are recorded for inter-disk fault. The analysis of fault severity, which is obatined in this paper, is an advantage of the proposed measurement technique. In this regard, the techniques from machine learning and numerical analysis are employed to train a predictive engine for smart interpretation of FRA data. It is revealed that the proposed intelligent technique is capable of interpreting, detecting, and classifying the transformer winding inter-disk fault and its severity. The new introduced FRA measurement setup is also able to support the online FRA data assessment.

AB - Transformers are utilized in generation, transmission, and distribution power system network, and face an enormous number of hazards during their course of operation. Frequency response analysis (FRA) is an inexpensive, accurate, and non-destructive technique to explore the transformer mechanical integrity very fast. However, FRA results interpretation is not being automated yet. This study introduces a new setup for FRA measurement that can assist to leave the conventional FRA data interpretation techniques and obtain smart interpretation. Hence, FRA setups and interpretation techniques are studied and formulated in this paper. A new measurement technique is introduced and discussed in detail. Practical studies are performed over distribution and power transformers and FRA data are recorded for inter-disk fault. The analysis of fault severity, which is obatined in this paper, is an advantage of the proposed measurement technique. In this regard, the techniques from machine learning and numerical analysis are employed to train a predictive engine for smart interpretation of FRA data. It is revealed that the proposed intelligent technique is capable of interpreting, detecting, and classifying the transformer winding inter-disk fault and its severity. The new introduced FRA measurement setup is also able to support the online FRA data assessment.

KW - FRA interpretation

KW - Frequency response analysis (FRA)

KW - online monitoring

KW - short-circuit detection

UR - http://www.scopus.com/inward/record.url?scp=85069971546&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85069971546&partnerID=8YFLogxK

U2 - 10.1109/TPWRD.2019.2909144

DO - 10.1109/TPWRD.2019.2909144

M3 - Article

AN - SCOPUS:85069971546

VL - 34

SP - 1508

EP - 1519

JO - IEEE Transactions on Power Delivery

JF - IEEE Transactions on Power Delivery

SN - 0885-8977

IS - 4

M1 - 8682123

ER -