N. Narasimhulu,D.V. Ashok Kumar,M. Vijaya Kumar,




DBN,EKF,linear load,non-linear load,ANN,NFS,harmonic coefficients,HIF,


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 etc


I Bokka Krishna Chaitanya, Anamika Yadav and Mohammad Pazoki, “An Intelligent Detection of High-Impedance Faults for Distribution Lines Integrated with Distributed Generators”, IEEE Systems Journal, Vol. 14, No. 1, pp. 870 – 879, March 2020

II Chengye Lu, Sheng Wu, Chunxiao Jiang and Jinfen, “Weak Harmonic Signal Detection Methodin Chaotic Interference based on Extended Kalman Filter”, Digital Communications and Networks, Vol.5, No.1, pp.51-55, February 2019

III Érica Mangueira Lima, Núbia Silva Dantas Brito and Benemar Alencar de Souza, “High impedance fault detection based on Stockwell transform and third harmonic current phase angle”, Electric Power Systems Research, Vol.175, pp.1-14, October 2019,

IV Junbo Zhao, Marcos Netto and Lamine Mili, “A Robust Iterated Extended Kalman Filter for Power System Dynamic State Estimation”, IEEE Transactions on Power Systems, Vol. 32, No. 4, pp. 3205 – 3216, July 2017

V J.U.N. Nunes, A.S. Bretas, N.G. Bretas, A.R. Herrera-Orozco and L.U. Iurinice, “Distribution systems high impedance fault location: A spectral domain model considering parametric error processing”, Elsevier, International Journal of Electrical Power & Energy Systems, Vol. 109, pp. 227-241, July 2019

VI Kumari Sarwagya, Sourav De and Paresh Kumar Nayak, “High-impedance fault detection in electrical power distribution systems using moving sum approach”, IET Science, Measurement & Technology, Vol. 12, No. 1, pp. 1-8, 2018

VII Meera R.Karamta and J.G.Jamnani, “Implementation of Extended Kalman Filter Based Dynamic State Estimation on SMIB System Incorporating UPFC Dynamics”, Energy Procedia, Vol.100, pp. 315-324, November 2016

VIII MuhammadSarwar, FaisalMehmood, Muhammad Abid, Abdul QayyumKhan, Sufi TabassumGul and Adil SarwarKhan, “High impedance fault detection and isolation in power distribution networks using support vector machines”, Journal of King Saud University – Engineering Sciences, July 2019

IX Sinha, Pampa, and Manoj Kumar Maharana, “Artificial Intelligence in Classifying High Impedance Faults in Electrical Power Distribution System”, In proceedings of International Conference on Recent Trends in Computing, Communication and Networking Technologies (ICRTCCNT’19), Kings Engineering College, pp.1-5, 2019

X VicenteTorres-Garcia, DanielGuillen, JimenaOlveres, BorisEscalante-Ramirez and Juan R.Rodriguez-Rodriguez, “Modelling of high impedance faults in distribution systems and validation based on multiresolution techniques”, Computers & Electrical Engineering, Vol. 83, pp.1-15, May 2020

XI Yuming Hua, Junhai Guo and Hua Zhao, “Deep Belief Networks and deep learning”, In Proceedings of International Conference on Intelligent Computing and Internet of Things, pp.1-4, 2015

View Download