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...
Saved in:
Main Authors: | , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
2020
|
Online Access: | http://dspace.uniten.edu.my/jspui/handle/123456789/13072 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Universiti Tenaga Nasional |
Language: | English |
id |
my.uniten.dspace-13072 |
---|---|
record_format |
dspace |
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 |