Publication:
Gas Turbine Performance Monitoring and Operation Challenges: A Review

Simple item page
dc.citedby1
dc.contributor.authorYousif S.en_US
dc.contributor.authorAlnaimi F.en_US
dc.contributor.authorThiruchelvam S.en_US
dc.contributor.authorid57211393920en_US
dc.contributor.authorid58027086700en_US
dc.contributor.authorid55812442400en_US
dc.date.accessioned2024-10-14T03:18:38Z
dc.date.available2024-10-14T03:18:38Z
dc.date.issued2023
dc.description.abstractGas turbines efficiently produce high amounts of electrical power hence they have been widely deployed as dependable power generators. It has been detected that the performance of gas turbines is a function of plenty of operational parameters and environmental variables. The impacts of those variables on the said performance can be mitigated using powerful monitoring techniques. Thus, extra maintenance costs, component defect costs, and manpower costs can be illuminated. This paper has enlisted the factors impacting gas turbine efficiency. It has also reviewed multiple monitoring solutions for the said impacting factors, It has been concluded that all types of sensors have ignored errors in their work, which may exacerbate the problems of malfunctions in gas turbines due to the critical environment in which they operate (heat, fumes, etc.)en_US
dc.description.abstracthowever, the machine learning-based monitoring systems excel in addressing such problems. The most cost-effective and accurate monitoring task can be achieved by using machine learning and deep learning tools. � 2023, Gazi Universitesi. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.35378/gujs.948875
dc.identifier.epage171
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85150684594
dc.identifier.spage154
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85150684594&doi=10.35378%2fgujs.948875&partnerID=40&md5=6ec780a42a3bf060caa30abb6bc49018
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34250
dc.identifier.volume36
dc.pagecount17
dc.publisherGazi Universitesien_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGold Open Access
dc.sourceScopus
dc.sourcetitleGazi University Journal of Science
dc.subjectFault
dc.subjectGas Turbine
dc.subjectMachine learning
dc.subjectSensor
dc.subjectSwirl
dc.subjectCost effectiveness
dc.subjectDeep learning
dc.subjectGases
dc.subjectLearning systems
dc.subjectElectrical power
dc.subjectFault
dc.subjectGas turbine performance
dc.subjectMachine-learning
dc.subjectOperational parameters
dc.subjectParameter variable
dc.subjectPerformance
dc.subjectPerformance-monitoring
dc.subjectPower
dc.subjectSwirl
dc.subjectGas turbines
dc.titleGas Turbine Performance Monitoring and Operation Challenges: A Reviewen_US
dc.typeReviewen_US
dspace.entity.typePublication
Files
Collections
SCOPUS