An EMD based fault type identification scheme in transmission line

Fault classification in distance protection of transmission lines, with considering the wide variation in the fault operating conditions, has been very challenging task. In this paper, an approach is presented to classify the fault in transmission line based on Empirical Mode Decomposition (EMD) using instantaneous power for each phase of only one terminal. For decision making stage of proposed methodology, three famous algorithms (Artificial neural network, support vector machine and decision tree) are used and it is shown that support vector machine demonstrate a suitable approach for selecting the faulty phase/phases. The results denote that suggested scheme is independent of effects of variation of fault inception angle, fault location, fault resistance, fault type and noise in current and voltage signals and also the proposed method is able to classify all the faults on transmission line within half cycle after the inception of fault.

[1]  M. Melamed.  Detection , 2021, SETI: Astronomy as a Contact Sport.

[2]  Kesab Bhattacharya,et al.  Detection, Classification, and Estimation of Fault Location on an Overhead Transmission Line Using S-transform and Neural Network , 2015 .

[3]  P.B.D. Gupta,et al.  Application of RBF neural network to fault classification and location in transmission lines , 2004 .

[4]  A.K. Sinha,et al.  A comparison of Fourier transform and wavelet transform methods for detection and classification of faults on transmission lines , 2006, 2006 IEEE Power India Conference.

[5]  N.S.D. Brito,et al.  Fault detection and classification in transmission lines based on wavelet transform and ANN , 2006, IEEE Transactions on Power Delivery.

[6]  Sami Ekici,et al.  Support Vector Machines for classification and locating faults on transmission lines , 2012, Appl. Soft Comput..

[7]  Mohammad Amin Jarrahi,et al.  Novel voltage control method in distribution networks with DG resources , 2015, 2015 2nd International Conference on Knowledge-Based Engineering and Innovation (KBEI).

[8]  Haidar Samet,et al.  A comparison between SVC and STATCOM in flicker mitigation of electric arc furnace using practical recorded data , 2015, 2015 30th International Power System Conference (PSC).

[9]  Haidar Samet,et al.  An ANFIS-Based Fault Classification Approach in Double-Circuit Transmission Line Using Current Samples , 2015, IWANN.

[10]  Mohammad Amin Jarrahi,et al.  The combination of load shedding and removal of capacitors in under frequency situations , 2015, 2015 2nd International Conference on Knowledge-Based Engineering and Innovation (KBEI).

[11]  N. Ramesh Babu,et al.  Fault classification in power systems using EMD and SVM , 2017 .

[12]  Abdul Gafoor Shaik,et al.  A new wavelet based fault detection, classification and location in transmission lines , 2015 .

[13]  Mladen Kezunovic,et al.  Transmission-Line Fault Analysis Using Synchronized Sampling , 2014, IEEE Transactions on Power Delivery.

[14]  Sukumar Mishra,et al.  Empirical-Mode Decomposition With Hilbert Transform for Power-Quality Assessment , 2009 .

[15]  Hassan Fathabadi,et al.  Two novel proposed discrete wavelet transform and filter based approaches for short-circuit faults detection in power transmission lines , 2015, Appl. Soft Comput..

[16]  A Jamehbozorg,et al.  A Decision-Tree-Based Method for Fault Classification in Single-Circuit Transmission Lines , 2010, IEEE Transactions on Power Delivery.