Paper Contents
Abstract
This review summarizes studies on the topic, Advanced Monitoring and Control Systems for Smart Transformer Applications in Modern Power Grids to fill an urgent gap of improved reliability and integration of transformers across more complex grids. The purpose of the review was to consider real-time monitoring methods, compare the integrations of IoT and digital twins, examine predictive maintenance applications based on machine learning, compare adaptive control schemes, and find challenges related to implementation. A literature review of various sources that used IoT sensors, digital twins, machine learning models, and adaptive protection designs was carried out, with particular focus on experimental verification and field implementation. Among the key conclusions, IoT-enables real-time monitoring and edge computing dramatically increase fault detection accuracy and responsiveness; digital twin systems and multimodal AI models enable predictive maintenance and operational optimisation despite computational and cybersecurity limitations; machine learning algorithms are helpful to predict transformer health, but substantial amounts of high-quality data and effective deployment strategies are still needed; adaptive control schemes can bring dynamic grid stability improvements, but standardisation and hardware complexity present bottlenecks. All these results indicate that advanced monitoring and control systems that are integrated improve significantly the transformer functioning and grid stability. The review highlights the demand to standardize, scale, and secure solutions that can enable a broader adoption to guide future research and practical applications to smart grid settings in the changing environments.
Copyright
Copyright © 2025 Anikeze Nnamdi Ogbonna, Engr. Dr. Onuigbo chika Martha, Marcel N. Eze, Okelue Daniel E. This is an open access article distributed under the Creative Commons Attribution License.
