Fault Diagnosis on a Wound Rotor Induction Generator Using Probabilistic Intelligence

Elsie F. Swana; Wesley Doorsamy

doi:10.1109/EEEIC.2018.8494409

Fault Diagnosis on a Wound Rotor Induction Generator Using Probabilistic Intelligence

Elsie F. Swana
, Wesley Doorsamy

Institute for Intelligent Systems

University of Johannesburg

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

Wound rotor induction generators are commonly used for wind applications. Although this technology is mature and in widespread use, there has been relatively little research on online condition monitoring thereof towards improving overall reliability of the system in which it is applied. This paper presents a method for diagnosing incipient faults on a wound rotor induction generator. The proposed method uses a probabilistic intelligence technique Bayesian classification together with voltage signature analysis for the fault diagnosis which has yet to be presented for wound rotor induction generators. A model of a three-phase wound rotor induction generator is constructed using finite element modelling. The behaviour of the generator is investigated under healthy, stator fault and rotor fault conditions. The proposed method is then implemented and tested for the task of diagnosing these faults. Results indicate that the Nave Bayes classifier was successfully trained and yielded 94% test accuracy which indicates the potential suitability of the method in enhancing predictive maintenance for wound rotor induction generators.

Original language	English
Title of host publication	Proceedings - 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538651858
DOIs	https://doi.org/10.1109/EEEIC.2018.8494409
Publication status	Published - 16 Oct 2018
Event	2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2018 - Palermo, Italy Duration: 12 Jun 2018 → 15 Jun 2018

Publication series

Name	Proceedings - 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2018

Conference

Conference	2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2018
Country/Territory	Italy
City	Palermo
Period	12/06/18 → 15/06/18

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Bayesian classification
Fault diagnosis
Intelligent condition monitoring
wound-rotor induction generator

ASJC Scopus subject areas

Energy Engineering and Power Technology
Renewable Energy, Sustainability and the Environment
Electrical and Electronic Engineering
Industrial and Manufacturing Engineering
Environmental Engineering
Hardware and Architecture

Access to Document

10.1109/EEEIC.2018.8494409

Cite this

Swana, E. F., & Doorsamy, W. (2018). Fault Diagnosis on a Wound Rotor Induction Generator Using Probabilistic Intelligence. In Proceedings - 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2018 Article 8494409 (Proceedings - 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EEEIC.2018.8494409

Swana, Elsie F. ; Doorsamy, Wesley. / Fault Diagnosis on a Wound Rotor Induction Generator Using Probabilistic Intelligence. Proceedings - 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2018. Institute of Electrical and Electronics Engineers Inc., 2018. (Proceedings - 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2018).

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title = "Fault Diagnosis on a Wound Rotor Induction Generator Using Probabilistic Intelligence",

abstract = "Wound rotor induction generators are commonly used for wind applications. Although this technology is mature and in widespread use, there has been relatively little research on online condition monitoring thereof towards improving overall reliability of the system in which it is applied. This paper presents a method for diagnosing incipient faults on a wound rotor induction generator. The proposed method uses a probabilistic intelligence technique Bayesian classification together with voltage signature analysis for the fault diagnosis which has yet to be presented for wound rotor induction generators. A model of a three-phase wound rotor induction generator is constructed using finite element modelling. The behaviour of the generator is investigated under healthy, stator fault and rotor fault conditions. The proposed method is then implemented and tested for the task of diagnosing these faults. Results indicate that the Nave Bayes classifier was successfully trained and yielded 94\% test accuracy which indicates the potential suitability of the method in enhancing predictive maintenance for wound rotor induction generators.",

keywords = "Bayesian classification, Fault diagnosis, Intelligent condition monitoring, wound-rotor induction generator",

author = "Swana, \{Elsie F.\} and Wesley Doorsamy",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2018 ; Conference date: 12-06-2018 Through 15-06-2018",

year = "2018",

month = oct,

day = "16",

doi = "10.1109/EEEIC.2018.8494409",

language = "English",

series = "Proceedings - 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Swana, EF & Doorsamy, W 2018, Fault Diagnosis on a Wound Rotor Induction Generator Using Probabilistic Intelligence. in Proceedings - 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2018., 8494409, Proceedings - 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2018, Institute of Electrical and Electronics Engineers Inc., 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2018, Palermo, Italy, 12/06/18. https://doi.org/10.1109/EEEIC.2018.8494409

Fault Diagnosis on a Wound Rotor Induction Generator Using Probabilistic Intelligence. / Swana, Elsie F.; Doorsamy, Wesley.
Proceedings - 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2018. Institute of Electrical and Electronics Engineers Inc., 2018. 8494409 (Proceedings - 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Fault Diagnosis on a Wound Rotor Induction Generator Using Probabilistic Intelligence

AU - Swana, Elsie F.

AU - Doorsamy, Wesley

PY - 2018/10/16

Y1 - 2018/10/16

N2 - Wound rotor induction generators are commonly used for wind applications. Although this technology is mature and in widespread use, there has been relatively little research on online condition monitoring thereof towards improving overall reliability of the system in which it is applied. This paper presents a method for diagnosing incipient faults on a wound rotor induction generator. The proposed method uses a probabilistic intelligence technique Bayesian classification together with voltage signature analysis for the fault diagnosis which has yet to be presented for wound rotor induction generators. A model of a three-phase wound rotor induction generator is constructed using finite element modelling. The behaviour of the generator is investigated under healthy, stator fault and rotor fault conditions. The proposed method is then implemented and tested for the task of diagnosing these faults. Results indicate that the Nave Bayes classifier was successfully trained and yielded 94% test accuracy which indicates the potential suitability of the method in enhancing predictive maintenance for wound rotor induction generators.

AB - Wound rotor induction generators are commonly used for wind applications. Although this technology is mature and in widespread use, there has been relatively little research on online condition monitoring thereof towards improving overall reliability of the system in which it is applied. This paper presents a method for diagnosing incipient faults on a wound rotor induction generator. The proposed method uses a probabilistic intelligence technique Bayesian classification together with voltage signature analysis for the fault diagnosis which has yet to be presented for wound rotor induction generators. A model of a three-phase wound rotor induction generator is constructed using finite element modelling. The behaviour of the generator is investigated under healthy, stator fault and rotor fault conditions. The proposed method is then implemented and tested for the task of diagnosing these faults. Results indicate that the Nave Bayes classifier was successfully trained and yielded 94% test accuracy which indicates the potential suitability of the method in enhancing predictive maintenance for wound rotor induction generators.

KW - Bayesian classification

KW - Fault diagnosis

KW - Intelligent condition monitoring

KW - wound-rotor induction generator

UR - https://www.scopus.com/pages/publications/85056553349

U2 - 10.1109/EEEIC.2018.8494409

DO - 10.1109/EEEIC.2018.8494409

M3 - Conference contribution

AN - SCOPUS:85056553349

T3 - Proceedings - 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2018

BT - Proceedings - 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2018

Y2 - 12 June 2018 through 15 June 2018

ER -

Swana EF, Doorsamy W. Fault Diagnosis on a Wound Rotor Induction Generator Using Probabilistic Intelligence. In Proceedings - 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2018. Institute of Electrical and Electronics Engineers Inc. 2018. 8494409. (Proceedings - 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2018). doi: 10.1109/EEEIC.2018.8494409

Fault Diagnosis on a Wound Rotor Induction Generator Using Probabilistic Intelligence

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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