Abstract
Fault Diagnosis of Oil-Immersed Power Transformer Based on Difference-Mutation Brain Storm Optimized Catboost Model
Highlights
Transformer is vital equipment of power system, capable of achieving voltage transformation, power distribution and power transmission
To address the problem of low accuracy of power transformer fault diagnosis, this study proposed a transformer fault diagnosis method based on difference-mutation Brain Storm Optimization Algorithm (DBSO)-CatBoost model
The original five-dimensional data, the data formed by the dimension reduction based on ratio method combined with Kernel principal component analysis (KPCA), the data formed by the dimension reduction based on ratio method combined with principal component analysis (PCA), and the data formed by the dimension reduction based on ratio method combined with partial least squares (PLS) were used to form four different data sets with four different data processing methods
Summary
Transformer is vital equipment of power system, capable of achieving voltage transformation, power distribution and power transmission. MEI ZHANG et al.: Fault Diagnosis of Oil-immersed Power Transformer Based on Difference-mutation Brain Storm Optimized Catboost Model(September 2021). Zhang et al combined the optimized BP neural network with DGA method to increase the accuracy of transformer fault detection to a certain extent, while defects remain (e.g., slow training speed and difficult parameter determination) [18]. Huang Tongxiang et al used a support vector machine for transformer fault diagnosis Such a machine exhibited strong learning generalization ability, whereas the accuracy is not high when there are many fault types and information is missing [19]. As demonstrated from the comparative experiments of two literatures, the accuracy of the transformer fault diagnosis based on GBDT could be higher than that of non-ensemble learning algorithm [21],[22].
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