Intelligent Fault Discrimination in Power Transformers: A Comprehensive Review of Methods

The reliable discrimination between magnetizing inrush currents and internal faults is essential for effective power transformer protection and has a direct impact on the security and stability of modern power systems. Although the second-harmonic restraint method has been widely adopted in transformer differential protection, its dependability can be affected by several operating conditions, including asymmetric energization, current transformer saturation, and the use of modern low-loss cores with reduced harmonic content. This paper presents a comprehensive and critical review of advanced techniques for distinguishing inrush currents from internal faults. The reviewed methods are classified into five main methodological categories: harmonic-based methods, time-domain approaches, signal-processing techniques, artificial intelligence-based schemes, and hybrid strategies. For each category, the fundamental operating principles, key advantages, and inherent limitations are discussed. A comparative assessment is also provided to highlight the trade-offs among detection accuracy, operating speed, robustness under adverse conditions, and practical implementation feasibility. The review shows a clear shift toward intelligent and data-driven protection schemes that combine effective feature extraction or deep learning with fast decision-making mechanisms. However, several challenges remain, particularly in relation to cross-site generalization, guaranteed response time, and hardware implementation constraints. Finally, the paper outlines a future research agenda for adaptive and computationally efficient transformer protection, emphasizing the need for benchmark datasets that include field cases, reproducible evaluation protocols, and the co-design of protection algorithms with embedded hardware platforms.