Condition Monitoring of Electrical Transformers Using the Internet of Things: A Systematic Literature Review

Featured Application: IoT-based transformer monitoring enhances predictive maintenance, reduces downtime, and optimizes power grid reliability and efficiency in smart grids. The adoption of Internet of Things (IoT) technology for transformer condition monitoring is increasingly replacing traditional methods. This systematic review aims to evaluate the existing research on IoT frameworks used in transformer condition monitoring, providing insights into their effectiveness and research trends. This review seeks to identify the leading IoT frameworks employed in transformer condition monitoring; analyze the key research objectives, methods, and outcomes; and assess the global research distribution and technological tools used in this field. A systematic literature review was conducted by searching published databases using keywords related to “Internet of Things”, “transformers”, “condition monitoring”, and “fault diagnosis”. The search spanned publications released between 2014 and 2024, yielding 262 articles. Of these, 120 met the predefined review criteria and were included for further analysis. This review found that Arduino boards are the most used microcontrollers for monitoring and analyzing transformer operational parameters, with Arduino IDE 1.8 being the predominant software for programming. The primary research focus in the reviewed literature is the identification of transformer faults. The geographical distribution of research contributions shows that India leads with 65% of the studies, followed by China (11%) and Pakistan (5%). The findings indicate a strong global interest in developing IoT-based transformer condition monitoring systems, particularly in India. This review highlights the potential of IoT technologies to enhance transformer monitoring and diagnostics. The insights gained from this review can guide future research and the development of more advanced IoT frameworks for transformer condition monitoring.