An Intelligent Monitoring Interface for a Coal-Fired Power Plant Boiler Trips
A power plant monitoring system embedded with artificial intelligence can enhance its effectiveness by reducing the time spent in trip analysis and follow up procedures. Experimental results showed that Multilayered perceptron neural network trained with Levenberg-Marquardt (LM) algorithm achieved t...
| Main Author: | |
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| Format: | Thesis |
| Published: |
Curtin University
2018
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| Online Access: | http://hdl.handle.net/20.500.11937/77234 |
| _version_ | 1848763831512203264 |
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| author | Mohd Nistah, Nong Nurnie |
| author_facet | Mohd Nistah, Nong Nurnie |
| author_sort | Mohd Nistah, Nong Nurnie |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | A power plant monitoring system embedded with artificial intelligence can enhance its effectiveness by reducing the time spent in trip analysis and follow up procedures. Experimental results showed that Multilayered perceptron neural network trained with Levenberg-Marquardt (LM) algorithm achieved the least mean squared error of 0.0223 with the misclassification rate of 7.435% for the 10 simulated trip prediction. The proposed method can identify abnormality of operational parameters at the confident level of ±6.3%. |
| first_indexed | 2025-11-14T11:09:43Z |
| format | Thesis |
| id | curtin-20.500.11937-77234 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:09:43Z |
| publishDate | 2018 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-772342019-12-10T05:32:41Z An Intelligent Monitoring Interface for a Coal-Fired Power Plant Boiler Trips Mohd Nistah, Nong Nurnie A power plant monitoring system embedded with artificial intelligence can enhance its effectiveness by reducing the time spent in trip analysis and follow up procedures. Experimental results showed that Multilayered perceptron neural network trained with Levenberg-Marquardt (LM) algorithm achieved the least mean squared error of 0.0223 with the misclassification rate of 7.435% for the 10 simulated trip prediction. The proposed method can identify abnormality of operational parameters at the confident level of ±6.3%. 2018 Thesis http://hdl.handle.net/20.500.11937/77234 Curtin University fulltext |
| spellingShingle | Mohd Nistah, Nong Nurnie An Intelligent Monitoring Interface for a Coal-Fired Power Plant Boiler Trips |
| title | An Intelligent Monitoring Interface for a Coal-Fired Power Plant Boiler Trips |
| title_full | An Intelligent Monitoring Interface for a Coal-Fired Power Plant Boiler Trips |
| title_fullStr | An Intelligent Monitoring Interface for a Coal-Fired Power Plant Boiler Trips |
| title_full_unstemmed | An Intelligent Monitoring Interface for a Coal-Fired Power Plant Boiler Trips |
| title_short | An Intelligent Monitoring Interface for a Coal-Fired Power Plant Boiler Trips |
| title_sort | intelligent monitoring interface for a coal-fired power plant boiler trips |
| url | http://hdl.handle.net/20.500.11937/77234 |