To address these limitations, this study proposes an intelligent transformer protection framework that integrates relay automation with machine learning (ML) algorithms for real-time fault detection, classification, and isolation. Taking the 500 kVA intelligent substation in Shenzhen. Transformers play a crucial role in modern power systems by enabling efficient voltage transformation and energy distribution across transmission and distribution networks. Their continuous operation and protection are vital to maintain grid reliability and economic stability. Existing solutions are constrained by a trade-off: sensitivity is compromised when setting values are. With 52% of transformer failures caused by insulation degradation, aging and electrical abnormalities such as through faults, extending the life of these devices through early detection or even prediction of these failure models has become a top priority for power system engineers.
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