Fault diagnosis basis and characteristic determination of oil-immersed transformer based on deep learning
This paper introduces a fault diagnosis method for oil-immersed transformers based on the deep forest model. This method analyzes the data of dissolved gases in transformer oil and utilizes the multi-level processing capabilities of the deep forest model to accurately extract fault features from high-dimensional data to identify and predict potential faults. Comparative analysis shows that compared with the traditional three-ratio method and BP neural network,this model has significantly improved the accuracy and reliability of fault diagnosis. The research results not only verify the effectiveness of the deep forest model in processing complex transformer data,but also provide technical support for transformer maintenance and management,enhancing the stability and security of the power system.
oil-immersed transformerfault diagnosisdeep forest modelpower system security
万方数据
维普
