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Development of intelligent early warning system for steam turbine

Fault detection and diagnosis is a critical element in the power generation sector. Early faults detection ensures that correct mitigation measures can be taken, whilst false alarms should be eschewed to avoid unnecessary cost of operation, interruption and downtime. Modern power plant is equipped w...

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Main Authors: Ismail Alnaimi, F.B., Bin Ismail, R.I., Ker, P.J., Wahidin, S.K.B.
Format: Article
Language:English
Published: 2020
Online Access:http://dspace.uniten.edu.my/jspui/handle/123456789/13072
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spelling my.uniten.dspace-130722020-09-18T04:24:48Z Development of intelligent early warning system for steam turbine Ismail Alnaimi, F.B. Bin Ismail, R.I. Ker, P.J. Wahidin, S.K.B. Fault detection and diagnosis is a critical element in the power generation sector. Early faults detection ensures that correct mitigation measures can be taken, whilst false alarms should be eschewed to avoid unnecessary cost of operation, interruption and downtime. Modern power plant is equipped with thousands of sensors for monitoring, diagnosis and sensor validation application. By utilizing these features, we can use the collected operational data to develop a data-driven condition monitoring method. Intelligent Early Warning System (IEWS) represented by Artificial Neural Network (ANN), which was developed by training the network with real operational data, can be proven useful for real-time monitoring of a power plant. In this work, an integrated data preparation method was proposed. The ANN models and the hybrid artificial intelligence (AI) of ANN with Genetic Algorithm (GA), which is able to detect steam turbine trip for Malaysia Jana Manjung (MNJ) power station were developed. The AI models adopting ANN and GA were trained with real data from the MNJ station. The developed models were capable of detecting the specific trip earlier before the actual trip occurrence was detected by the existing control system. The AI model provides a good opportunity for further research and implementation of AI in the power generation industry especially in fault detection and diagnosis initiatives. © School of Engineering, Taylor’s University. 2020-02-03T03:30:12Z 2020-02-03T03:30:12Z 2019 Article http://dspace.uniten.edu.my/jspui/handle/123456789/13072 en
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
language English
description Fault detection and diagnosis is a critical element in the power generation sector. Early faults detection ensures that correct mitigation measures can be taken, whilst false alarms should be eschewed to avoid unnecessary cost of operation, interruption and downtime. Modern power plant is equipped with thousands of sensors for monitoring, diagnosis and sensor validation application. By utilizing these features, we can use the collected operational data to develop a data-driven condition monitoring method. Intelligent Early Warning System (IEWS) represented by Artificial Neural Network (ANN), which was developed by training the network with real operational data, can be proven useful for real-time monitoring of a power plant. In this work, an integrated data preparation method was proposed. The ANN models and the hybrid artificial intelligence (AI) of ANN with Genetic Algorithm (GA), which is able to detect steam turbine trip for Malaysia Jana Manjung (MNJ) power station were developed. The AI models adopting ANN and GA were trained with real data from the MNJ station. The developed models were capable of detecting the specific trip earlier before the actual trip occurrence was detected by the existing control system. The AI model provides a good opportunity for further research and implementation of AI in the power generation industry especially in fault detection and diagnosis initiatives. © School of Engineering, Taylor’s University.
format Article
author Ismail Alnaimi, F.B.
Bin Ismail, R.I.
Ker, P.J.
Wahidin, S.K.B.
spellingShingle Ismail Alnaimi, F.B.
Bin Ismail, R.I.
Ker, P.J.
Wahidin, S.K.B.
Development of intelligent early warning system for steam turbine
author_facet Ismail Alnaimi, F.B.
Bin Ismail, R.I.
Ker, P.J.
Wahidin, S.K.B.
author_sort Ismail Alnaimi, F.B.
title Development of intelligent early warning system for steam turbine
title_short Development of intelligent early warning system for steam turbine
title_full Development of intelligent early warning system for steam turbine
title_fullStr Development of intelligent early warning system for steam turbine
title_full_unstemmed Development of intelligent early warning system for steam turbine
title_sort development of intelligent early warning system for steam turbine
publishDate 2020
url http://dspace.uniten.edu.my/jspui/handle/123456789/13072
_version_ 1678595890374246400
score 13.222552

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