Recognition of Fused Partial Discharge Patterns in High Voltage Insulation Systems: A Hybrid DCNN and SVM Based Approach

The performance assessment of insulation materials within the components of a high voltage power system depends upon effective condition monitoring techniques of the insulation. One of the important phenomena to be considered in the diagnostics and performance assessment of high voltage insulation materials is the Partial Discharge (PD) measurement. The improved Deep learning techniques help in the effective identification and classification of various sources of PD mechanism. In this work, Deep Convolution Neural Network (DCNN) technique is proposed for the fusion of several features that are extracted from the PD signals. Three-dimensional Phase Resolved patterns of PD images can be used for training the DNN. For the classification of PD patterns, linear and non-linear type of multi-class support vector machine has been proposed. The results obtained are compared with linear SVM and non-linear SVMs with the polynomial kernel, Radial Basis Function (RBF) kernel, and Sigmoidal kernel. The SVM type with a higher pattern recognition rate is identified to be effective in PD pattern recognition and classification. The results of the proposed work show that the fusion approach of PD patterns supports applying huge PD data sets as input, generated by multiple faults, for effective PD pattern recognition.