An Intelligent Monitoring Interface for a Coal-Fired Power Plant Boiler Trips
| dc.contributor.author | Mohd Nistah, Nong Nurnie | |
| dc.contributor.supervisor | Lenin Gopal | en_US |
| dc.date.accessioned | 2019-12-10T05:32:41Z | |
| dc.date.available | 2019-12-10T05:32:41Z | |
| dc.date.issued | 2018 | en_US |
| dc.identifier.uri | http://hdl.handle.net/20.500.11937/77234 | |
| dc.description.abstract |
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%. | en_US |
| dc.publisher | Curtin University | en_US |
| dc.title | An Intelligent Monitoring Interface for a Coal-Fired Power Plant Boiler Trips | en_US |
| dc.type | Thesis | en_US |
| dcterms.educationLevel | MPhil | en_US |
| curtin.department | Department of Mechanical Engineering | en_US |
| curtin.accessStatus | Open access | en_US |
| curtin.faculty | Curtin Malaysia | en_US |