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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Taylor's University
2023
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my.uniten.dspace-247042023-05-29T15:26:04Z Development of intelligent early warning system for steam turbine Ismail Alnaimi F.B. Bin Ismail R.I. Ker P.J. Wahidin S.K.B. 58027086700 57210826033 37461740800 57210827167 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. Final 2023-05-29T07:26:04Z 2023-05-29T07:26:04Z 2019 Article 2-s2.0-85071650301 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071650301&partnerID=40&md5=80d5e3a33ec3804cb548978b1b9176ac https://irepository.uniten.edu.my/handle/123456789/24704 14 2 844 858 Taylor's University Scopus |
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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. |
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58027086700 |
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58027086700 Ismail Alnaimi F.B. Bin Ismail R.I. Ker P.J. Wahidin S.K.B. |
| 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_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 |
| publisher |
Taylor's University |
| publishDate |
2023 |
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1806428435694747648 |
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