Utilizing AI-Driven DevOps for Predictive Maintenance and Anomaly Detection in Smart Grids.
DOI:
https://doi.org/10.46243/jst.2025.v10.i04.pp27-33Keywords:
Anomaly detection, Predictive maintenance,, Grid management, AI applications, Smart grid, ML algorithms, SCADA, Edge computing, DevOps principle, Scalability,, DevOps practices, AI-driven DevOpsAbstract
The research presents an analysis of the enhancement process of AI-driven DevOps in grid management by modifying
anomaly detection, predictive maintenance and entire system effectiveness. It involves an AI driven continuous
feedback loop between these two areas, to get the best delivery and upgrades of the AI models. This collaboration
helps improve system reliability as well as speed up the deployment of needed updates, requiring the smart grid to run
as close to optimal as possible.
References
Meliani, M., Barkany, A.E., Abbassi, I.E., Darcherif, A.M. and Mahmoudi, M., 2021. Energy management in the smart grid: State-ofthe-art and future trends. International Journal of Engineering Business Management, 13, p.18479790211032920
Kumar, A., Alaraj, M., Rizwan, M. and Nangia, U., 2021. Novel AI based energy management system for smart grid with RES integration. IEEE Access, 9, pp.162530-162542
Keleko, A.T., Kamsu-Foguem, B., Ngouna, R.H. and Tongne, A., 2022. Artificial intelligence and real-time predictive maintenance in industry 4.0: a bibliometric analysis. AI and Ethics, 2(4), pp.553-577
Tyagi, A., 2021. Intelligent DevOps: Harnessing Artificial Intelligence to Revolutionize CI/ CD Pipelines and Optimize Software Delivery Lifecycles. Journal of Emerging Technologies and Innovative Research, 8, pp.367-385
Zha, D., Bhat, Z.P., Lai, K.H., Yang, F., Jiang, Z., Zhong, S. and Hu, X., 2025. Data-centric artificial intelligence: A survey. ACM Computing Surveys, 57(5), pp.1-42
Berhane, T., Melese, T., Walelign, A. and Mohammed, A., 2023. A Hybrid Convolutional Neural Network and Support Vector Machine-Based Credit Card Fraud Detection Model. Mathematical Problems in Engineering, 2023(1), p.8134627
Walker, C.M., Agarwal, V., Nistor, J., Ramuhalli, P. and Muhheim, M., 2023. Assessment of Cloud-Based Applications Enabling a Scalable Risk-Informed Predictive Maintenance Strategy Across the Nuclear Fleet (No. INL/RPT-23- 74696). Idaho National Laboratory (INL), Idaho Falls, ID (United States)
Hossain, M.E., Tarafder, M.T.R., Ahmed, N., Al Noman, A., Sarkar, M.I. and Hossain, Z., 2023. Integrating AI with Edge Computing and Cloud Services for Real-Time Data Processing and Decision Making. International Journal of Multidisciplinary Sciences and Arts, 2(4), pp.252-261
Borghesi, A., Burrello, A. and Bartolini, A., 2021. Examon-x: a predictive maintenance framework Lakshmi Prasad Rongali: Utilizing AI-Driven DevOps for Predictive Maintenance and Anomaly Detection in Smart Grids for automatic monitoring in industrial iot systems. IEEE Internet of Things Journal, 10(4), pp.2995-3005
Tanikonda, A., Katragadda, S.R., Peddinti, S.R. and Pandey, B.K., 2021. Integrating AI-Driven Insights into DevOps Practices. Journal of Science & Technology, 2(1)
Jha, R.K., 2023. Cybersecurity and confidentiality in smart grid for enhancing sustainability and reliability. Recent Research Reviews Journal, 2(2), pp.215-241
Ahmad, T., Madonski, R., Zhang, D., Huang, C. and Mujeeb, A., 2022. 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, 160, p.112128
Vadde, B.C. and Munagandla, V.B., 2022. AI-Driven Automation in DevOps: Enhancing Continuous Integration and Deployment. International Journal of Advanced Engineering Technologies and Innovations, 1(3), pp.183-193
Moreno Escobar, J.J., Morales Matamoros, O., Tejeida Padilla, R., Lina Reyes, I. and Quintana Espinosa, H., 2021. A comprehensive review on smart grids: Challenges and opportunities. Sensors, 21(21), p.6978
Jakaria, A.H.M., Rahman, M.A. and Gokhale, A., 2021. Resiliency-aware deployment of SDN in smart grid SCADA: A formal synthesis model. IEEE Transactions on Network and Service Management, 18(2), pp.1430-1444
Hammad, A. and Abu-Zaid, R., 2024. Applications of AI in Decentralized Computing Systems: Harnessing Artificial Intelligence for Enhanced Scalability, Efficiency, and Autonomous Decision-Making in Distributed Architectures. Applied Research in Artificial Intelligence and Cloud Computing, 7, pp.161-187
Sevilla, F.R.S., Liu, Y., Barocio, E., Korba, P., Andrade, M., Bellizio, F., Bos, J., Chaudhuri, B., Chavez, H., Cremer, J. and Eriksson, R., 2022. State-of-the-art of data collection, analytics, and future needs of transmission utilities worldwide to account for the continuous growth of sensing data. International journal of electrical power & energy systems, 137, p.107772
Barmpounakis, S., Maroulis, N., Koursioumpas, N., Kousaridas, A., Kalamari, A., Kontopoulos, P. and Alonistioti, N., 2022. AI-driven, QoS prediction for V2X communications in beyond 5G systems. Computer Networks, 217, p.109341
Samariya, D. and Thakkar, A., 2023. A comprehensive survey of anomaly detection algorithms. Annals of Data Science, 10(3), pp.829-850
Kisten, M., Ezugwu, A.E.S. and Olusanya, M.O., 2024. Explainable artificial intelligence model for predictive maintenance in smart agricultural facilities. IEEE Access, 12, pp.24348-24367
Allan, B., Oldfield, R., Doutriaux, C., Lewis, K., Ahrens, J., Sims, B., Sweeney, C., Banesh, D. and Wofford, Q., 2023. Assessment of Data-Management Infrastructure Needs for Production Use of Advanced Machine Learning and Artificial Intelligence: Tri-Lab Level II Milestone (8554) (No. SAND-2023-08642). Sandia National Laboratories (SNL), Albuquerque, NM, and Livermore, CA (United States)
Al-Marsy, A., Chaudhary, P. and Rodger, J.A., 2021. A model for examining challenges and opportunities in use of cloud computing for health information systems. Applied System Innovation, 4(1), p.15
