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dc.contributor.authorMohd Nistah, Nong Nurnie
dc.contributor.supervisorLenin Gopalen_US
dc.date.accessioned2019-12-10T05:32:41Z
dc.date.available2019-12-10T05:32:41Z
dc.date.issued2018en_US
dc.identifier.urihttp://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%.

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dc.publisherCurtin Universityen_US
dc.titleAn Intelligent Monitoring Interface for a Coal-Fired Power Plant Boiler Tripsen_US
dc.typeThesisen_US
dcterms.educationLevelMPhilen_US
curtin.departmentDepartment of Mechanical Engineeringen_US
curtin.accessStatusOpen accessen_US
curtin.facultyCurtin Malaysiaen_US


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