Intelligent transmission line fault diagnosis using the Apriori associated rule algorithm under cloud computing environment - Al-Jumaili A.H.A.; Muniyandi R.C.; Hasan M.K.; Singh M.J.; Paw J.K.S. (Frontier Scientific Publishing, 2024)
Electric 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 algorithms 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 systems security. 2023 by author(s). Journal of Autonomous Intelligence is published by Frontier Scientific Publishing.