Systematic Literature Review Penerapan Artificial Intelligence dalam Pemeliharaan Prediktif Sistem Tenaga Listrik sebagai Inovasi Pembelajaran Berbasis Teknologi Digital

Rachmatul Hidayathika; Zada Aulia Munawarah; Umar Hamzah; Didik Aribowo; Novaldi Ramdani Reza

doi:10.62951/prosemnasipi.v3i1.220

Authors

Rachmatul Hidayathika Universitas Sultan Ageng Tirtayasa
Zada Aulia Munawarah Universitas Sultan Ageng Tirtayasa
Umar Hamzah Universitas Sultan Ageng Tirtayasa
Didik Aribowo Universitas Sultan Ageng Tirtayasa
Novaldi Ramdani Reza Universitas Sultan Ageng Tirtayasa

DOI:

https://doi.org/10.62951/prosemnasipi.v3i1.220

Keywords:

Artificial Intelligence, Condition Monitoring, Machine Learning, Power Systems, Predictive Maintenancce

Abstract

Artificial Intelligence (AI) has become an important technology in predictive maintenance of power systems due to its ability to improve reliability, efficiency, and asset management. Conventional maintenance approaches, such as corrective and preventive maintenance, often fail to accurately predict equipment failures, resulting in higher operational costs and unplanned outages. This study aims to analyze the development, applications, benefits, challenges, and future directions of AI in predictive maintenance of power systems. The research employed a Systematic Literature Review (SLR) based on the PRISMA framework. Literature was collected from Google Scholar using keywords related to artificial intelligence, predictive maintenance, machine learning, fault diagnosis, condition monitoring, and power systems. A total of 22 publications published between 2020 and 2025 met the inclusion criteria and were analyzed. The findings indicate that AI plays a significant role in fault detection, fault diagnosis, condition monitoring, and remaining useful life prediction of power equipment. AI has been widely applied to transformers, generators, switchgear, Photovoltaic Systems, and variable frequency drives. Furthermore, the integration of AI with IoT, Big Data Analytics, Cloud Computing, and Digital Twin technologies enhances predictive accuracy and maintenance decision-making. Overall, AI contributes significantly to improving the reliability, efficiency, and sustainability of future power systems.

References

Alsumaidaee, Y. A. M., Yaw, C. T., Koh, S. P., Tiong, S. K., Chen, C. P., & Ali, K. (2022). Review of medium-voltage switchgear fault detection in a condition-based monitoring system by using Deep Learning. Energies, 15(18), 6762. https://doi.org/10.3390/en15186762

Aminifar, F., Abedini, M., Amraee, T., et al. (2022). A review of power system protection and asset management with machine learning techniques. Energy Systems, 13(4), 855–892. https://doi.org/10.1007/s12667-021-00448-6

Amraoui, T., Meradi, S., & Amrane, A. (2025). Machine learning for predictive maintenance of electrical machines: A review. Proceedings of the International Conference on Intelligent Computer Systems, Data Science and Applications (IC2SDA), 1–8. https://doi.org/10.1109/IC2SDA68097.2025.11331563

Bajwa, A., Tonoy, A. A. R., & Khan, M. A. M. (2025). IoT-enabled condition monitoringin power transformers: A proposed model. Review of Applied Science and Technology, 4(2). Available at SSRN: https://ssrn.com/abstract=5341323

Bazi, S., Nhaila, H., & El Khaili, M. (2024). Artificial intelligence for diagnosing power transformer faults. Proceedings of the 4th International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET), 1–8. https://doi.org/10.1109/IRASET60544.2024.10548561

Bechara, H., et al. (2024). Review of artificial intelligence methods for faults monitoring, diagnosis, and prognosis in hydroelectric synchronous generators. IEEE Access, 12, 173599–173617. https://doi.org/10.1109/ACCESS.2024.3502546

Benitez, I. B., & Pahati, M. D. (2025). Transformative AI technologies in high-voltage systems: A review of advances in predictive maintenance, fault detection, and grid optimization. Proceedings of the IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET), 368–373. https://doi.org/10.1109/IICAIET67254.2025.11265217

Cui, Y., Butler, R., Liu, Z., Jaremko, T., & Hinds, S. (2025). The future of predictive maintenance for variable frequency drives based on AI/ML. IEEE Transactions on Industry Applications, 61(4), 6833–6841. https://doi.org/10.1109/TIA.2025.3550104

Esmaeili Nezhad, A., & Samimi, M. (2024). A review of the applications of machine learning in the condition monitoring of transformers. Energy Systems, 15(2), 463–493. https://doi.org/10.1007/s12667-022-00532-5

Fassi, Y., Heiries, V., Boutet, J., & Boisseau, S. (2024). Toward physics-informed machine-learning-based predictive maintenance for power converters—A review. IEEE Transactions on Power Electronics, 39(2), 2692–2720. https://doi.org/10.1109/TPEL.2023.3328438

Fylladitakis, E. D. (2025). Fault identification and predictive maintenance techniques for high-voltage equipment: A review and recent advances. Journal of Power and Energy Engineering, 13, 1–39. https://doi.org/10.4236/jpee.2025.137001

Gandhi, K., & Verma, P. (2024). Predictive maintenance for transformers and substation equipment using sensor time-series models. American Journal of Computing and Engineering, 7(5), 33–35.

Garcia, J., Rios-Colque, L., Peña, A., & Rojas, L. (2025). Condition monitoring and predictive maintenance in industrial equipment: An NLP-assisted review of signal processing, hybrid models, and implementation challenges. Applied Sciences, 15(10), 5465. https://doi.org/10.3390/app15105465

Habyarimana, M., & Adebiyi, A. A. (2025). A review of artificial intelligence applications in predicting faults in electrical machines. Energies, 18(7), 1616. https://doi.org/10.3390/en18071616

Khan, M. A. M. (2025). AI and machine learning in transformer fault diagnosis: A systematic review. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.5190585

Mansouri, M., Trabelsi, M., Nounou, H., & Nounou, M. (2021). Deep Learning-based fault diagnosis of Photovoltaic Systems: A comprehensive review and enhancement prospects. IEEE Access, 9, 126286–126306. https://doi.org/10.1109/ACCESS.2021.3110947

Nuruzzaman, M., Limon, G. Q., Chowdhury, A. R., & Khan, M. A. M. (2025). Predictive maintenance in power transformers: A systematic review of AI and IoT applications. ASRC Procedia: Global Perspectives in Science and Scholarship, 1(1), 34–47. https://doi.org/10.63125/r72yd809

Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., et al. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71

Paul, J., Lim, W. M., O'Cass, A., Hao, A. W., & Bresciani, S. (2021). Scientific procedures and rationales for systematic literature reviews (SPAR-4-SLR). International Journal of Consumer Studies, 45(4), O1–O16. https://doi.org/10.1111/ijcs.12695

Rana, S. (2025). AI-driven fault detection and predictive maintenance in electrical power systems: A systematic review of data-driven approaches, Digital Twins, and self-healing grids. American Journal of Advanced Technology and Engineering Solutions, 1(1), 258–289. https://doi.org/10.63125/4p25x993

Shamim, M. M. R., & Ruddro, R. A. (2025). Smart diagnostics in industrial maintenance: A systematic review of AI-enabled predictive maintenance tools and condition monitoring techniques. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.5269918

Sugiyono. (2021). Metode penelitian kuantitatif, kualitatif, dan R&D. Alfabeta.

Vichare, R. V., & Gaikwad, S. R. (2025). AI-based predictive maintenance of solar photovoltaics systems: A comprehensive review. Energy Informatics, 8, 128. https://doi.org/10.1186/s42162-025-00594-6

Zemouri, R. (2025). Power transformer prognostics and health management using machine learning: A review and future directions. Machines, 13(2), 125. https://doi.org/10.3390/machines13020125