Evolvable Transformer Fault Diagnosis Model Combining Feature Analysis and Machine Learning
Transformers,which possess complex mechanisms and exert an extensive influence,are important equipment in large power systems.The state detection and fault diagnosis of transformers are hence a key challenge in traditional power systems;they also constitute an important field for the application of intelligent algorithms in the era of intelligence.Existing intelligent fault diagnosis research is limited by the scarcity of fault samples,poor interpretability of diagnostic results,and difficulties in model updating.This paper proposes a transformer fault diagnosis model based on the dynamic analysis of time series flow data.First,a high-confidence fault data sample library with sequence features is built using manually assisted annotation and data augmentation methods,and a neural network model constituting a fusion temporal feature analyzer and multiple classifiers is constructed as the foundational model for training and analysis.A reasoning method based on similar cases is thus realized,using multidimensional distance measurement methods such as the distance similarity,pattern similarity,and shape similarity.This helps diagnose and classify real-time detection flow data for fault diagnosis and early warning,thereby helping operation and maintenance personnel conduct fault analysis based on historical experience and intelligent technology.Experimental verification confirms that the proposed method significantly improves the accuracy and interpretability of the fault diagnosis.The proposed method is applied to real scenarios of online transformer fault diagnosis.
transformerstream data analysisfault diagnosis modelevolutionary mechanismcase reasoning
万方数据
维普
