A hybrid Wavelet-ANN protection scheme for series compensated EHV transmission line

The paper presents a novel idea of protection of the multi-terminal Extra High Voltage (EHV) transmission line having multiple Series compensation. A statistical learning perspective for improved classification of faults using Artificial Neural Networks (ANN) has been proposed. The protective scheme uses single end cur-rent data of three phases of line to detect and classify faults. A Multiresolution Analysis (MRA) wavelet transform is employed to decompose the signals acquired and further processed to extract statistical features. The statistical features learning algorithm utilizes a set of ANN structures with a different combination of Neural Network parameters to determine the best ANN topology for Classifier. The algorithm generates different fault patterns arising out of different fault scenarios and altering system parameters in the test system. The features are selected based on ANOVA F-test statistics to determine relevance and improve classification accuracy. The features thus selected from fault patterns are given to the Hybrid Wavelet-ANN structure. The ANN once trained on a part of data set is later tested on the other part of unseen patterns and further validated on rest of the patterns. To provide a comparative Support Vector Machine Classifier is used to classify the fault patterns. A 5 fold cross validation is used on the data set to check the accuracy of SVM. It is shown that the proposed method using Pattern Recognition using Hybrid structure provides a high accuracy with reliability in identifying and classifying fault patterns as opposed to SVM.