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

Ahmed Hadi Ali AL-Jumaili, Ravie Chandren Muniyandi, Mohammad Kamrul Hasan, Mandeep Jit Singh, Johnny Koh Siaw Paw

Abstract


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 informationalisation 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.


Keywords


Cloud Computing; Artificial Intelligent Algorithms; Power Systems; Smart Grid; Big Data

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References


1. Li X, Liu H, Wang W, et al. Big data analysis of the internet of things in the digital twins of smart city based on deep learning. Future Generation Computer Systems 2022; 128: 167–177. doi: 10.1016/j.future.2021.10.006.

2. Liu R. Research on big data acquisition and application of power energy based on big data cloud platform. Proceedings of the SPIE 2022; 12168: 1216834. doi: 10.1117/12.2630992.

3. Paukstadt U, Becker J. Uncovering the business value of the internet of things in the energy domain—A review of smart energy business models. Electronic Markets 2019; 31: 51–66. doi: 10.1007/s12525-019-00381-8.

4. Al-Jumaili AHA, Al Mashhadany YI, Sulaiman R, Alyasseri ZAA. A conceptual and systematics for intelligent power management system-based cloud computing: Prospects, and challenges. Applied Sciences 2021; 11(21): 9820. doi: 10.3390/APP11219820.

5. Ahmad T, Madonski R, Zhang D, et al. Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm. Renewable and Sustainable Energy Reviews 2022; 160: 112128. doi: 10.1016/j.rser.2022.112128.

6. Seeger PM, Yahouni Z, Alpan G. Literature review on using data mining in production planning and scheduling within the context of cyber physical systems. Journal of Industrial Information Integration 2022; 28: 100371. doi: 10.1016/j.jii.2022.100371.

7. Ren D, Hao X, Zhao Y, Wang M. Research on power big data analysis technology and application based on cloud computing. Lecture Notes in Electrical Engineering 2022; 827: 1117–1123. doi: 10.1007/978-981-16-8052-6_154.

8. Gui P, Zhang M. Human resource management of energy companies based on big data analysis. Mathematical Problems in Engineering 2022; 2022: 5489369. doi: 10.1155/2022/5489369.

9. Al-Nuaymy AI, Hamed WA, Ali AH. The role of social media in enhancing the learning process of Iraq students. Journal of Engineering and Applied Sciences 2019; 14(19): 7142–7153. doi: 10.36478/JEASCI.2019.7142.7153.

10. Ali AH, Mohammad OKJ. Impacting of the e-platforms on the 4.0th industrial educational revolution. In: Proceedings of ICIST 2019: The 9th International Conference on Information Systems and Technologies; 2019 Mar 24–26; Cairo. New York: Association for Computing Machinery; 2019.

11. Ji B, Wang Y, Song K, et al. A survey of computational intelligence for 6G: Key technologies, applications and trends. IEEE Transactions on Industrial Informatics 2021; 17(10): 7145–7154. doi: 10.1109/TII.2021.3052531.

12. Yang C, Huang Q, Li Z, et al. Big data and cloud computing: Innovation opportunities and challenges. International Journal of Digital Earth 2017; 10(1): 13–53. doi: 10.1080/17538947.2016.1239771.

13. Qin Y, Yang W, Ai X, Chen L. Fault tolerant storage and data access optimization in data center networks. Journal of Network and Computer Applications 2018; 113: 109–118. doi: 10.1016/j.jnca.2018.04.001.

14. Wang S, Zhong Y, Wang E. An integrated GIS platform architecture for spatiotemporal big data. Future Generation Computer Systems 2019; 94: 160–172. doi: 10.1016/j.future.2018.10.034.

15. Madni SHHH, Latiff MSA, Coulibaly Y, Abdulhamid SM. Recent advancements in resource allocation techniques for cloud computing environment: A systematic review. Cluster Computing 2017; 20(3): 2489–2533. doi: 10.1007/s10586-016-0684-4.

16. Liu Y, Liang S, He C, et al. A cloud-computing and big data based wide area monitoring of power grids strategy. IOP Conference Series: Materials Science and Engineering 2019; 677(4): 042055. doi: 10.1088/1757-899X/677/4/042055.

17. Cheng Y, Chen K, Sun H, et al. Data and knowledge mining with big data towards smart production. Journal of Industrial Information Integration 2018; 9: 1–13. doi: 10.1016/j.jii.2017.08.001.

18. Zhan F, Zhu X, Zhang L, et al. Summary of association rules. IOP Conference Series: Earth and Environmental Science 2019; 252: 032219. doi: 10.1088/1755-1315/252/3/032219.

19. Tan Q. Construction of multidimensional data knowledge base by improved classification association rule mining algorithm. Advances in Computer Science Research 2017; 59: 901–905. doi: 10.2991/emcm-16.2017.171.

20. Ye Y. Research on Apriori algorithm and its application in electronic commerce system. Advances in Social Science, Education and Humanities Research 2016; 63: 161–166. doi: 10.2991/amahs-16.2016.33.

21. Guo Z, Chen T. Improvement of Apriori algorithm based on vector and vertical array. In: Proceedings of 2016 International Conference on Control and Automation (ICCA 2016); 2016 Jun 1–3; Kathmandu. Lancaster: DEStech Transactions on Engineering and Technology Research; 2016.

22. Qiu H. Improvement parallelization in Apriori algorithm. In: Proceedings of 2020 International Conference on Computers, Information Processing and Advanced Education (CIPAE); 2020 Oct 16–18; Ottawa. New York: Association for Computing Machinery; 2020. p. 235–238.

23. Wang L, Meng J, Xu P, Peng K. Mining temporal association rules with frequent itemsets tree. Applied Soft Computing 2018; 62: 817–829. doi: 10.1016/j.asoc.2017.09.013.

24. Du Z. Energy analysis of internet of things data mining algorithm for smart green communication networks. Computer Communications 2020; 152: 223–231. doi: 10.1016/j.comcom.2020.01.046.

25. Yang Q, Fu Q, Wang C, Yang J. A matrix-based Apriori algorithm improvement. In: Proceedings of 2018 IEEE Third International Conference on Data Science in Cyberspace (DSC); 2018 Jun 18–21; Guangzhou. New York: IEEE; 2018. p. 824–828.

26. Wang H, Gao Y. Research on parallelization of Apriori algorithm in association rule mining. Procedia Computer Science 2021; 183: 641–647. doi: 10.1016/j.procs.2021.02.109.

27. Wang C, Zheng X. Application of improved time series Apriori algorithm by frequent itemsets in association rule data mining based on temporal constraint. Evolutionary Intelligence 2020; 13(1): 39–49. doi: 10.1007/s12065-019-00234-5.

28. Feng D, Zhu L, Zhang L. Research on improved Apriori algorithm based on MapReduce and HBase. In: Proceedings of 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC); 2016 Oct 3–5; Xi’an. New York: IEEE; 2017. p. 887–891.

29. Singh S, Garg R, Mishra PK. Review of Apriori based algorithms on map reduce framework. In: Proceedings of International Conference on Communication and Computing (ICC-2014); 2014 Jun 12–14; Bangalore. Amsterdam: Elsevier; 2014. p. 593–604.

30. Wang J, Zhang F, Liu H, et al. Interruptible load scheduling model based on an improved chicken swarm optimization algorithm. CSEE Journal of Power and Energy Systems 2021; 7(2): 232–240. doi: 10.17775/CSEEJPES.2020.01150.

31. Wu D, Xu S, Kong F. Convergence analysis and improvement of the chicken swarm optimization algorithm. IEEE Access 2016; 4: 9400–9412. doi: 10.1109/ACCESS.2016.2604738.

32. Deb S, Gao XZ, Tammi K, et al. Recent studies on chicken swarm optimization algorithm: A review (2014–2018). Artificial Intelligence Review 2020; 53(3): 1737–1765. doi: 10.1007/s10462-019-09718-3.

33. Sathyaraj R, Ramanathan L, Lavanya K, et al. Chicken swarm foraging algorithm for big data classification using the deep belief network classifier. Data Technologies and Applications 2020; 55(3): 332–352. doi: 10.1108/DTA-08-2019-0146.

34. Vinoth R, Ananth JP. Rider chicken optimization algorithm-based recurrent neural network for big data classification in spark architecture. The Computer Journal 2022; 65(8): 2183–2196. doi: 10.1093/comjnl/bxab053.

35. Liang X, Lv H, Guo F, Wang H. A cloud security situational awareness model based on parallel Apriori algorithm. Applied Mechanics and Materials 2014; 556–562: 6294–6297. doi: 10.4028/www.scientific.net/AMM.556-562.6294.

36. Singh S, Garg R, Mishra PK. Performance optimization of MapReduce-based Apriori algorithm on hadoop cluster. Computers & Electrical Engineering 2018; 67: 348–364. doi: 10.1016/j.compeleceng.2017.10.008.




DOI: https://doi.org/10.32629/jai.v6i1.640

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Copyright (c) 2023 Ahmed Hadi Ali AL-Jumaili, Ravie Chandren Muniyandi, Mohammad Kamrul Hasan, Mandeep Jit Singh, Johnny Koh Siaw Paw

License URL: https://creativecommons.org/licenses/by-nc/4.0