Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 14275-14283 |
| Number of pages | 9 |
| Journal | ARPN Journal of Engineering and Applied Sciences |
| Volume | 11 |
| Issue number | 24 |
| Publication status | Published - 01 Jan 2016 |
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All Science Journal Classification (ASJC) codes
- Engineering(all)
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Development and implementation of intelligent condition monitoring system for steam turbine trips. / Ismail, Firas Basim; Ismail, Rahmat Izaizi B.; Ker, Pin Jern.
In: ARPN Journal of Engineering and Applied Sciences, Vol. 11, No. 24, 01.01.2016, p. 14275-14283.Research output: Contribution to journal › Article
TY - JOUR
T1 - Development and implementation of intelligent condition monitoring system for steam turbine trips
AU - Ismail, Firas Basim
AU - Ismail, Rahmat Izaizi B.
AU - Ker, Pin Jern
PY - 2016/1/1
Y1 - 2016/1/1
N2 - 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.
AB - 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.
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M3 - Article
VL - 11
SP - 14275
EP - 14283
JO - ARPN Journal of Engineering and Applied Sciences
JF - ARPN Journal of Engineering and Applied Sciences
SN - 1819-6608
IS - 24
ER -