Rizwan A. Farade, Noor Izzri Abdul Wahab, Jeyraj Selvaraj, Husam S. Samkari, Mohammed F. Allehyani, Nehad Ali Shah, Maddina Dinesh Kumar, Zeeshan
Transformers encounter increasing thermal stress due to rising power densities and the integration of renewable energy. This makes it harder for traditional cooling fluids to work. This review's goal is to critically compare empirical studies of dielectric nanofluids as thermal media for power transformers. It integrates reported thermal performance of dielectric nanofluids in different nanoparticle and base fluid systems. Enhancements include thermal conductivity of 0.9-210%, flash point of 44%, fire point of 9%, and pour point of −45%, and reduced gassing tendency. These enhancements come with trade-offs such as specific heat reductions and viscosity increase. The mechanisms that control heat transport are analysed, with focus on nanoscale interfacial phenomena, percolation networks, and phonon transport. The review identifies and reconciles critical knowledge gaps, focussing on long-term stability and interfacial compatibility under thermal cycling. The novel contribution consists of the integration of interfacial physics, performance trade-offs, sustainability considerations, and intelligent monitoring into a cohesive framework. Sustainability is addressed through the exploration of nanoparticle green synthesis pathways. This article extends earlier reviews by adding nanofluids to smart cooling systems driven by Internet of Things (IoT) and artificial intelligence (AI). The main findings suggest that nanoparticle surface chemistry is necessary for thermal enhancement, AI-driven systems boost performance, and green synthesis supports sustainability in next-generation applications. Finally, it points out key areas of research, such as accelerated ageing protocols, standardised evaluation, and renewable-grid integration, to turn laboratory results into practical, sustainable transformer cooling solutions. © 2026 The Authors.
Advanced Lightning, Power and Energy Research (ALPER), Department of Electrical and Electronics Engineering, Faculty of Engineering, University Putra Malaysia, Serdang, 43400, Malaysia; Higher Institution Centre of Excellence (HICoE), UM Power Energy Dedicated Advanced Centre (UMPEDAC), Universiti Malaya, Jalan Pantai Baharu, Kuala Lumpur, 59990, Malaysia; Department of Mathematical Sciences, Saveetha School of Engineering, SIMATS, Saveetha Nagar, Thandalam, TamilNadu, Chennai, 602105, India; Department of Electrical Engineering, Faculty of Engineering, Universitas Negeri Padang, Padang, 25131, Indonesia; Department of Electrical Engineering, University of Tabuk, Tabuk, 47713, Saudi Arabia; Artificial Intelligence and Sensing Technologies Research Center, University of Tabuk, Tabuk, 47713, Saudi Arabia; Department of Mechanical Engineering, Sejong University, Seoul, 05006, South Korea; Department of Mathematics, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Tamil Nadu, Chennai, 602105, India; Department of Mathematics and Statistics, Bacha Khan University, Charsadda, KP, Pakistan
