Determining appropriate data analytics for transformer health monitoring

Jose Ignacio Aizpurua, Victoria M. Catterson, Brian G. Stewart, Stephen D.J. McArthur, Brandon Lambert, Bismark Ampofo, Gavin Pereira, James G. Cross

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

Abstract

Transformers are vital assets for the safe, reliable and cost-effective operation of nuclear power plants. The unexpected failure of a transformer can lead to different consequences ranging from a lack of export capability, with the corresponding economic penalties, to catastrophic failure, with the associated health, safety and economic effects. Condition monitoring techniques examine the health of the transformer periodically, with the aim to identify early indicators of anomalies. However, many transformer failures occur because diagnostic and monitoring models do not identify degraded conditions in time. Therefore, health monitoring is an essential component to transformer lifecycle management. Existing tools for transformer health monitoring use traditional dissolved gas analysis based diagnostics techniques. With the advance of prognostics and health management (PHM) applications, we can enhance traditional transformer health monitoring techniques using PHM analytics. The design of an appropriate data analytics system requires a multi-stage design process including: (i) specification of engineering requirements; (ii) characterization of existing data sources and analytics to identify complementary techniques; (iii) development of the functional specification of the analytics suite to formalize its behavior, and finally (iv) deployment, validation, and verification of the functional requirements in the final platform. Accordingly, in this paper we propose a transformer analytics suite which incorporates anomaly detection, diagnostics, and prognostics modules in order to complement existing tools for transformer health monitoring.
LanguageEnglish
Title of host publication10th International Topical Meeting on Nuclear Plant Instrumentation, Control and Human Machine Interface Technologies
Place of PublicationLa Grange Park
Pages1-11
Number of pages11
Publication statusAccepted/In press - 5 Apr 2017
Event10th International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, NPIC and HMIT 2017 - Hyatt Regency, San Francisco, United States
Duration: 11 Jun 201715 Jun 2017
http://npic-hmit2017.org/

Conference

Conference10th International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, NPIC and HMIT 2017
Abbreviated titleNPIC and HMIT 2017
CountryUnited States
CitySan Francisco
Period11/06/1715/06/17
Internet address

Fingerprint

Health
Monitoring
Specifications
Economic and social effects
Gas fuel analysis
Requirements engineering
Condition monitoring
Nuclear power plants
Economics
Costs

Keywords

  • data analytics
  • prognostics and health management
  • transformer
  • condition monitoring
  • insulation

Cite this

Aizpurua, J. I., Catterson, V. M., Stewart, B. G., McArthur, S. D. J., Lambert, B., Ampofo, B., ... Cross, J. G. (Accepted/In press). Determining appropriate data analytics for transformer health monitoring. In 10th International Topical Meeting on Nuclear Plant Instrumentation, Control and Human Machine Interface Technologies (pp. 1-11). La Grange Park.
Aizpurua, Jose Ignacio ; Catterson, Victoria M. ; Stewart, Brian G. ; McArthur, Stephen D.J. ; Lambert, Brandon ; Ampofo, Bismark ; Pereira, Gavin ; Cross, James G. / Determining appropriate data analytics for transformer health monitoring. 10th International Topical Meeting on Nuclear Plant Instrumentation, Control and Human Machine Interface Technologies. La Grange Park, 2017. pp. 1-11
@inproceedings{43f7de921fac48748c513b9f7df800cf,
title = "Determining appropriate data analytics for transformer health monitoring",
abstract = "Transformers are vital assets for the safe, reliable and cost-effective operation of nuclear power plants. The unexpected failure of a transformer can lead to different consequences ranging from a lack of export capability, with the corresponding economic penalties, to catastrophic failure, with the associated health, safety and economic effects. Condition monitoring techniques examine the health of the transformer periodically, with the aim to identify early indicators of anomalies. However, many transformer failures occur because diagnostic and monitoring models do not identify degraded conditions in time. Therefore, health monitoring is an essential component to transformer lifecycle management. Existing tools for transformer health monitoring use traditional dissolved gas analysis based diagnostics techniques. With the advance of prognostics and health management (PHM) applications, we can enhance traditional transformer health monitoring techniques using PHM analytics. The design of an appropriate data analytics system requires a multi-stage design process including: (i) specification of engineering requirements; (ii) characterization of existing data sources and analytics to identify complementary techniques; (iii) development of the functional specification of the analytics suite to formalize its behavior, and finally (iv) deployment, validation, and verification of the functional requirements in the final platform. Accordingly, in this paper we propose a transformer analytics suite which incorporates anomaly detection, diagnostics, and prognostics modules in order to complement existing tools for transformer health monitoring.",
keywords = "data analytics, prognostics and health management, transformer, condition monitoring, insulation",
author = "Aizpurua, {Jose Ignacio} and Catterson, {Victoria M.} and Stewart, {Brian G.} and McArthur, {Stephen D.J.} and Brandon Lambert and Bismark Ampofo and Gavin Pereira and Cross, {James G.}",
year = "2017",
month = "4",
day = "5",
language = "English",
pages = "1--11",
booktitle = "10th International Topical Meeting on Nuclear Plant Instrumentation, Control and Human Machine Interface Technologies",

}

Aizpurua, JI, Catterson, VM, Stewart, BG, McArthur, SDJ, Lambert, B, Ampofo, B, Pereira, G & Cross, JG 2017, Determining appropriate data analytics for transformer health monitoring. in 10th International Topical Meeting on Nuclear Plant Instrumentation, Control and Human Machine Interface Technologies. La Grange Park, pp. 1-11, 10th International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, NPIC and HMIT 2017, San Francisco, United States, 11/06/17.

Determining appropriate data analytics for transformer health monitoring. / Aizpurua, Jose Ignacio; Catterson, Victoria M.; Stewart, Brian G.; McArthur, Stephen D.J.; Lambert, Brandon; Ampofo, Bismark; Pereira, Gavin; Cross, James G.

10th International Topical Meeting on Nuclear Plant Instrumentation, Control and Human Machine Interface Technologies. La Grange Park, 2017. p. 1-11.

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

TY - GEN

T1 - Determining appropriate data analytics for transformer health monitoring

AU - Aizpurua, Jose Ignacio

AU - Catterson, Victoria M.

AU - Stewart, Brian G.

AU - McArthur, Stephen D.J.

AU - Lambert, Brandon

AU - Ampofo, Bismark

AU - Pereira, Gavin

AU - Cross, James G.

PY - 2017/4/5

Y1 - 2017/4/5

N2 - Transformers are vital assets for the safe, reliable and cost-effective operation of nuclear power plants. The unexpected failure of a transformer can lead to different consequences ranging from a lack of export capability, with the corresponding economic penalties, to catastrophic failure, with the associated health, safety and economic effects. Condition monitoring techniques examine the health of the transformer periodically, with the aim to identify early indicators of anomalies. However, many transformer failures occur because diagnostic and monitoring models do not identify degraded conditions in time. Therefore, health monitoring is an essential component to transformer lifecycle management. Existing tools for transformer health monitoring use traditional dissolved gas analysis based diagnostics techniques. With the advance of prognostics and health management (PHM) applications, we can enhance traditional transformer health monitoring techniques using PHM analytics. The design of an appropriate data analytics system requires a multi-stage design process including: (i) specification of engineering requirements; (ii) characterization of existing data sources and analytics to identify complementary techniques; (iii) development of the functional specification of the analytics suite to formalize its behavior, and finally (iv) deployment, validation, and verification of the functional requirements in the final platform. Accordingly, in this paper we propose a transformer analytics suite which incorporates anomaly detection, diagnostics, and prognostics modules in order to complement existing tools for transformer health monitoring.

AB - Transformers are vital assets for the safe, reliable and cost-effective operation of nuclear power plants. The unexpected failure of a transformer can lead to different consequences ranging from a lack of export capability, with the corresponding economic penalties, to catastrophic failure, with the associated health, safety and economic effects. Condition monitoring techniques examine the health of the transformer periodically, with the aim to identify early indicators of anomalies. However, many transformer failures occur because diagnostic and monitoring models do not identify degraded conditions in time. Therefore, health monitoring is an essential component to transformer lifecycle management. Existing tools for transformer health monitoring use traditional dissolved gas analysis based diagnostics techniques. With the advance of prognostics and health management (PHM) applications, we can enhance traditional transformer health monitoring techniques using PHM analytics. The design of an appropriate data analytics system requires a multi-stage design process including: (i) specification of engineering requirements; (ii) characterization of existing data sources and analytics to identify complementary techniques; (iii) development of the functional specification of the analytics suite to formalize its behavior, and finally (iv) deployment, validation, and verification of the functional requirements in the final platform. Accordingly, in this paper we propose a transformer analytics suite which incorporates anomaly detection, diagnostics, and prognostics modules in order to complement existing tools for transformer health monitoring.

KW - data analytics

KW - prognostics and health management

KW - transformer

KW - condition monitoring

KW - insulation

UR - http://npic-hmit2017.org/

M3 - Conference contribution book

SP - 1

EP - 11

BT - 10th International Topical Meeting on Nuclear Plant Instrumentation, Control and Human Machine Interface Technologies

CY - La Grange Park

ER -

Aizpurua JI, Catterson VM, Stewart BG, McArthur SDJ, Lambert B, Ampofo B et al. Determining appropriate data analytics for transformer health monitoring. In 10th International Topical Meeting on Nuclear Plant Instrumentation, Control and Human Machine Interface Technologies. La Grange Park. 2017. p. 1-11