Multilevel SVM and AI based Transformer Fault Diagnosis using the DGA Data

Abstract

The Dissolved Gas Analysis (DGA) is utilized as a test for the detection of incipient problems in transformers, and condition monitoring of transformers using software-based diagnosis tools has become crucial. This research uses dissolved gas analysis as an intelligent fault classification of a transformer. The Multilayer SVM technique is used to determine the classification of faults and the name of the gas. The learned classifier in the multilayer SVM is trained with the training samples and can classify the state as normal or fault state, which contains six fault categories. In this paper, polynomial and Gaussian functions are utilized to assess the effectiveness of SVM diagnosis. The results demonstrate that the combination ratios and graphical representation technique is more suitable as a gas signature and that the SVM with the Gaussian function outperforms the other kernel functions in diagnosis accuracy.

A Novel Fault Diagnosis Method for a Power Transformer Based on Multi-Scale Approximate Entropy and Optimized Convolutional Networks

“…When Scale = 1, the coarse-grained data are the original time series. When Scale = 2, the coarse-grained time series is formed by calculating the average of two consecutive time points, as defined in Equations ( 7) and (8).…”

“…However, ANNs suffer from slow convergence and susceptibility to local optima. In paper [ 8 ], the multi-layer SVM technique is used to determine the classification of the transformer faults and the name of the dissolved gas. The results demonstrate that combination ratios and the graphical representation technique are more suitable as a gas signature and that an SVM with a Gaussian function outperforms the other kernel functions in its diagnosis accuracy.…”