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 (Prosiding Seminar Nasional Ilmu Pendidikan, 2026)
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.