Development and implementation of intelligent condition monitoring system for steam turbine trips

Sustainable initiatives are increasingly getting attention from the research community and one of the aspects in achieving sustainable development is to enhance the efficiency and optimize the technology used to generate and utilize energy. Fault detection and diagnosis is a critical optimization fa...

Full description

Saved in:
Bibliographic Details
Main Authors: Alnaimi F.B.I., Ismail R.I.B., Ker P.J.
Other Authors: 58027086700
Format: Article
Published: Asian Research Publishing Network 2023
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Universiti Tenaga Nasional
id my.uniten.dspace-22915
record_format dspace
spelling my.uniten.dspace-229152023-05-29T14:13:22Z Development and implementation of intelligent condition monitoring system for steam turbine trips Alnaimi F.B.I. Ismail R.I.B. Ker P.J. 58027086700 57189236796 37461740800 Sustainable initiatives are increasingly getting attention from the research community and one of the aspects in achieving sustainable development is to enhance the efficiency and optimize the technology used to generate and utilize energy. Fault detection and diagnosis is a critical optimization factor in 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. Pure Intelligent Condition Monitoring System (ICMS) represented by artificial neural network (ANN), developed by training the network with real operational data, may be proven to be useful for realtime monitoring of a power plant. In this work, an integrated data preparation method has been proposed and the development of ANN models to detect steam turbine trip for Malaysia MNJ power station will be presented. Two models adopting feed forward with back propagation ANN were trained with real data from the MNJ station. The developed models were capable of detecting the specific trip within a period of 32 minutes before the actual trip occurrence, which is considered to provide good and satisfactory early fault detection. � 2006-2016 Asian Research Publishing Network (ARPN). All rights reserved. Final 2023-05-29T06:13:22Z 2023-05-29T06:13:22Z 2016 Article 2-s2.0-85009160454 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85009160454&partnerID=40&md5=3da3f8a8c6d986223d34717b4cd4fb0b https://irepository.uniten.edu.my/handle/123456789/22915 11 24 14275 14283 Asian Research Publishing Network Scopus
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/
description Sustainable initiatives are increasingly getting attention from the research community and one of the aspects in achieving sustainable development is to enhance the efficiency and optimize the technology used to generate and utilize energy. Fault detection and diagnosis is a critical optimization factor in 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. Pure Intelligent Condition Monitoring System (ICMS) represented by artificial neural network (ANN), developed by training the network with real operational data, may be proven to be useful for realtime monitoring of a power plant. In this work, an integrated data preparation method has been proposed and the development of ANN models to detect steam turbine trip for Malaysia MNJ power station will be presented. Two models adopting feed forward with back propagation ANN were trained with real data from the MNJ station. The developed models were capable of detecting the specific trip within a period of 32 minutes before the actual trip occurrence, which is considered to provide good and satisfactory early fault detection. � 2006-2016 Asian Research Publishing Network (ARPN). All rights reserved.
author2 58027086700
author_facet 58027086700
Alnaimi F.B.I.
Ismail R.I.B.
Ker P.J.
format Article
author Alnaimi F.B.I.
Ismail R.I.B.
Ker P.J.
spellingShingle Alnaimi F.B.I.
Ismail R.I.B.
Ker P.J.
Development and implementation of intelligent condition monitoring system for steam turbine trips
author_sort Alnaimi F.B.I.
title Development and implementation of intelligent condition monitoring system for steam turbine trips
title_short Development and implementation of intelligent condition monitoring system for steam turbine trips
title_full Development and implementation of intelligent condition monitoring system for steam turbine trips
title_fullStr Development and implementation of intelligent condition monitoring system for steam turbine trips
title_full_unstemmed Development and implementation of intelligent condition monitoring system for steam turbine trips
title_sort development and implementation of intelligent condition monitoring system for steam turbine trips
publisher Asian Research Publishing Network
publishDate 2023
_version_ 1806425857790574592