Progressive Hierarchical Diagnosis of Internal Faults in Power Transformers

The causes of power transformer internal faults are complex and varied, making accurate diagnosis difficult, and most of the existing diagnostic techniques remain at fault characterization. To realize the accurate localization of multiple types of faults, this paper proposes a progressive hierarchical diagnosis method for power transformer faults with improved gray wolf algorithm (IGWO) coupled with Least Squares Support Vector Machines (LSSVM), based on the advantage of high binary classification accuracy of LSSVM, and the establishment of a recursive mapping relationship between multistate quantities and fault characteristics by increasing the number of classification layers of the model and reducing the number of classifications in each layer. Firstly, the principles of IGWO and LSSVM are introduced to establish the progressive hierarchical, automatic diagnosis and localization model for power transformer faults. Secondly, based on the state quantities of 300 groups of power transformers, the kernel principal component analysis is used for dimensionality reduction, and the linearly independent eigenstate quantities are selected, discretization according to DL/T 1685-2017 Guidelines for Condition Evaluation of Oil-immersed Transformers. Progressive stratification and automatic diagnosis with the help of algorithmic models: the first layer diagnoses the faulty circuit, the second layer determines the faulty part, and the third layer clarifies the cause of the fault. Obtain the diagnostic accuracy of each classifier and the optimal combination of penalty coefficients and kernel function parameters, and analyze and compare the fault diagnosis results with other algorithmic models. Finally, the validity of the methodology is verified with actual failure cases. The results show that the diagnostic model proposed in this paper possesses higher accuracy and faster computing speed than other methods.