Fault diagnosis of intelligent substation relay protection system based on transformer architecture and migration training model - Yao Mei; Saisai Ni; Haibo Zhang (Energy Informatics)
Abstract In the context of global energy transformation, the construction of smart grids is becoming a novel vogue in the evolution of power systems. As the core node of the smart grid, the efficient operation of the intelligent substation relay protection system is essential to the safety and stability of the power system. However, with the expansion of power grid-scale and complexity, traditional relay protection systems need help with fault diagnosis accuracy and response speed. This study proposes a fault diagnosis scheme of an intelligent substation relay protection system based on Transformer architecture and migration training model, aiming at improving the intelligent level of fault diagnosis. By introducing the Transformer architecture, the model can efficiently process high-dimensional and nonlinear complex data of substations, significantly improving the accuracy of fault pattern recognition from 82% of the original model to 96%, and the response speed is also increased by 30%. At the same time, using transfer learning technology, the adaptability and generalization capabilities of the model in new scenarios have been significantly enhanced, reducing the dependence on a large amount of new data and accelerating the deployment of the model among different substations. The experimental results show that this scheme can quickly and accurately identify various fault types and effectively locate fault points. This study not only promotes the development of intelligent technology for power systems but also lays a solid foundation for the safe and stable operation of smart grids and the sustainable development of the power industry.