International Journal of Scientific Research and Engineering Development

Power transformers are critical high-value assets whose unexpected failures lead to costly outages, grid instability, and safety risks. Recent advances in the Internet of Things (IoT), cloud platforms, and smart supervisory control and data acquisition (SCADA) systems have enabled real-time transformer condition monitoring and predictive maintenance. This paper provides a methodological review of the study “Condition Monitoring in Power Transformers Using IoT: A Model for Predictive Maintenance” by Bajwa, Tonoy, and Khan (2025), evaluating its data acquisition design, sensing architecture, predictive model, and integration potential within modern reliability engineering frameworks. While the original model offers a promising IoT-enabled monitoring structure, this review identifies several gaps related to sensor calibration, data pre-processing, cybersecurity, and SCADA interoperability. The paper proposes an improved methodology incorporating reliability-centered maintenance (RCM), failure mode and effects analysis (FMEA), data-driven anomaly detection, and secure cloud-edge architectures. Findings highlight that IoTbased transformer monitoring becomes significantly more effective when integrated with SCADA redundancy, real-time analytics, and reliability engineering tools. The review concludes with a set of recommendations for strengthening predictive maintenance models in power utility environments.