Authors:
N. Narasimhulu,D.V. Ashok Kumar,M. Vijaya Kumar,DOI NO:
https://doi.org/10.26782/jmcms.2020.08.00023Keywords:
DBN,EKF,linear load,non-linear load,ANN,NFS,harmonic coefficients,HIF,Abstract
In the paper, identification and classification of high impedance faults (HIF) are analyzed with the Extended Kalman filter and Deep Belief Neural Network (DBN). Here, the proposed method is utilized for classifying the HIF in power system. To extract the features of the signals, EKF is introduced and the DBN is used for classify the signals. Initially, the distribution system, the No Fault (NF) signals are analyzed. After that, in the distribution system linear load and non-linear loads are applied to the system. In this proposed method, radial distribution system and meshed distribution systems are analyzed under the HIF conditions. Here, harmonic coefficients of 3rd, 5th, 7th, 9th and 13th are analyzed with the help of proposed method. The feature signals of current and voltage under the harmonic components are taken as the input of DBN. The feature signals are classified with the help of DBN classifier. The proposed method is implemented in MATLAB/Simulink working platform and the detection performance evaluated. The evaluated results are compared with Artificial Neural Network (ANN) and Neuro Fuzzy Controller (NFC) methods. In addition, the proposed method is tested with the statistical measures like, Accuracy, Sensitivity, and Specificity etcRefference:
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