Design and Research of Transformer Fault Diagnosis Method Based on Data-driven

With the continuous development of smart substations, higher requirements are put forward for the level of transformer intelligence. By continuously optimizing transformer online monitoring and fault diagnosis technology to improve its level of intelligence, to meet the safe, efficient and stable operation of the power system. Based on the analysis of the gas composition in transformer oil, this article uses data as the driving force to apply artificial intelligence neural network algorithms to transformer fault diagnosis. At the same time, in view of the deficiencies of the neural network algorithm, the genetic algorithm is used to optimize the neural network to improve the performance of transformer fault diagnosis. The simulation verification is carried out in the MATLAB environment, and the optimized BP neural network algorithm is compared with the traditional BP neural network algorithm. The simulation results show that the neural network model optimized by genetic algorithm can play a better role in transformer fault diagnosis.