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Intelligent transmission line fault diagnosis using the Apriori associated rule algorithm under cloud computing environment

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dc.citedby1
dc.contributor.authorAl-Jumaili A.H.A.en_US
dc.contributor.authorMuniyandi R.C.en_US
dc.contributor.authorHasan M.K.en_US
dc.contributor.authorSingh M.J.en_US
dc.contributor.authorPaw J.K.S.en_US
dc.contributor.authorid58701334000en_US
dc.contributor.authorid14030355800en_US
dc.contributor.authorid55057479600en_US
dc.contributor.authorid58765817900en_US
dc.contributor.authorid58168727000en_US
dc.date.accessioned2024-10-14T03:19:58Z
dc.date.available2024-10-14T03:19:58Z
dc.date.issued2023
dc.description.abstractElectric power production data has the characteristics of massive data scale, high update frequency and fast growth rate. It is significant to process and analyse electric power production data to diagnose a fault. High levels of informa-tionalisation and intellectualization can be achieved in the actual details of developing a Power Plant Fault Diagnosis Management System. Furthermore, cloud computing technology and association rule mining as the core technology based on analysis of domestic and foreign research. In this paper, the optimised Apriori association rule algorithm is used as technical support to realise the function of interlocking fault diagnosis in the intelligent fault diagnosis system module. Hadoop distributed architecture is used to design and implement the power private cloud computing cluster. The functions of private cloud computing clusters for power extensive data management and analysis are realised through MapReduce computing framework and Hbase database. The leakage fault cases verify the algorithm�s applicability and complete the correlation diagnosis of water wall leakage fault. Through analysing the functional requirements of the system in the project, using MySQL database and Enhancer platform, the intelligent fault diagnosis management system of cloud computing power plant is designed and developed, which realises the functions of system modules such as system authority management, electronic equipment account, technical supervision, expert database, data centre. The result shows that the proposed method improves the security problem of the system, the message-digest algorithm (MD5) is used to encrypt the user password, and a strict role authorisation system is designed to realise the access and manage the system�s security. � 2023 by author(s). Journal of Autonomous Intelligence is published by Frontier Scientific Publishing.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.32629/jai.v6i1.640
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85175471600
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85175471600&doi=10.32629%2fjai.v6i1.640&partnerID=40&md5=d9c3533f78ddb88331236ecaed878229
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34465
dc.identifier.volume6
dc.publisherFrontier Scientific Publishingen_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGold Open Access
dc.sourceScopus
dc.sourcetitleJournal of Autonomous Intelligence
dc.subjectArtificial Intelligent Algorithms
dc.subjectBig Data
dc.subjectCloud Computing
dc.subjectPower Systems
dc.subjectSmart Grid
dc.titleIntelligent transmission line fault diagnosis using the Apriori associated rule algorithm under cloud computing environmenten_US
dc.typeArticleen_US
dspace.entity.typePublication
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