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Development of intelligent early warning system for steam turbine

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dc.citedby2
dc.contributor.authorIsmail Alnaimi F.B.en_US
dc.contributor.authorBin Ismail R.I.en_US
dc.contributor.authorKer P.J.en_US
dc.contributor.authorWahidin S.K.B.en_US
dc.contributor.authorid58027086700en_US
dc.contributor.authorid57210826033en_US
dc.contributor.authorid37461740800en_US
dc.contributor.authorid57210827167en_US
dc.date.accessioned2023-05-29T07:26:04Z
dc.date.available2023-05-29T07:26:04Z
dc.date.issued2019
dc.description.abstractFault 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.en_US
dc.description.natureFinalen_US
dc.identifier.epage858
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85071650301
dc.identifier.spage844
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85071650301&partnerID=40&md5=80d5e3a33ec3804cb548978b1b9176ac
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24704
dc.identifier.volume14
dc.publisherTaylor's Universityen_US
dc.sourceScopus
dc.sourcetitleJournal of Engineering Science and Technology
dc.titleDevelopment of intelligent early warning system for steam turbineen_US
dc.typeArticleen_US
dspace.entity.typePublication
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