Data‐Driven Fault Diagnosis Method for Power Transformers Using Modified Kriging Model

Abstract

A data-driven fault diagnosis method that combines Kriging model and neural network is presented and is further used for power transformers based on analysis of dissolved gases in oil. In order to improve modeling accuracy of Kriging model, a modified model that replaces the global model of Kriging model with BP neural network is presented and is further extended using linearity weighted aggregation method. The presented method integrates characteristics of the global approximation of the neural network technology and the localized departure of the Kriging model, which improves modeling accuracy. Finally, the validity of this method is demonstrated by several numerical computations of transformer fault diagnosis problems.

Research on Distributed 5G Signal Coverage Detection Algorithm Based on PSO-BP-Kriging

“…Jia et al found that Kriging interpolation can better reflect the spatial distribution characteristics of the target region, but the accuracy of neural network interpolation is higher [31]. In [32], an improved model using BP neural network technology instead of Kriging global model is proposed, which is further extended by linear weighted aggregation method to improve the modeling accuracy. Katsuaki et al proposed a neural Kriging interpolation method, which reproduces the spatial characteristics of regionalized variables and improves the interpolation accuracy to some extent in [33].…”