Detection, Classification, and Estimation of Fault Location on an Overhead Transmission Line Using S-transform and Neural Network

Abstract This article demonstrates a technique for diagnosis of fault type and faulty phase on overhead transmission lines. A method for computation of fault location is also incorporated in this work. The proposed method is based on the multi-resolution S-transform, which is used for generating complex S-matrices of the current signals measured at the sending and receiving ends of the line. The peak magnitude of the absolute value of every S-matrix is noted. The phase angle corresponding to every peak component is obtained from the argument of the relevant S-matrix. These features are used as input vectors of a probabilistic neural network for fault detection and classification. Detection of faulty phase(s) is followed by estimation of fault location. The voltage signal of the affected phase is processed to generate the S-matrix. The frequency components of the S-matrices for different fault locations are used as input vectors for training a back-propagation neural network. The results are obtained with satisfactory accuracy and speed. All the simulations have been done in MATLAB (The MathWorks, Natick, Massachusetts, USA) environment for different values of fault locations, fault resistances, and fault inception angles. The effect of noise on both the current and voltage signals has been investigated.

[1]  Doaa Khalil Ibrahim,et al.  Real time evaluation of DWT-based high impedance fault detection in EHV transmission , 2010 .

[2]  Thai Nguyen,et al.  Transmission Line Fault Type Classification Based on Novel Features and Neuro-fuzzy System , 2010 .

[3]  Mamdouh Abdel-Akher,et al.  Fault Analysis of Multiphase Distribution Systems Using Symmetrical Components , 2010, IEEE Transactions on Power Delivery.

[4]  N Perera,et al.  Recognition of Fault Transients Using a Probabilistic Neural-Network Classifier , 2011, IEEE Transactions on Power Delivery.

[5]  Mojtaba Khederzadeh,et al.  New Combined Method for Fault Detection, Classification, and Location in Series-compensated Transmission Line , 2012 .

[6]  Yanwei Wang,et al.  Fast Discrete Orthonormal Stockwell Transform , 2009, SIAM J. Sci. Comput..

[7]  A. A. Yusuffa,et al.  Fault location in a series compensated transmission line based on wavelet packet decomposition and support vector regression , 2015 .

[8]  C. P. Gupta,et al.  Discrete Wavelet Transform and Genetic Algorithm based Fault Classification of Transmission Systems , 2022 .

[9]  M. Sanaye-Pasand,et al.  High-Frequency Transients-Based Protection of Multiterminal Transmission Lines Using the SVM Technique , 2013, IEEE Transactions on Power Delivery.

[10]  Adisa A. Jimoh,et al.  Fault location in transmission lines based on stationary wavelet transform, determinant function feature and support vector regression , 2014 .

[11]  Subhransu Ranjan Samantaray,et al.  Decision tree-based fault zone identification and fault classification in flexible AC transmissions-based transmission line , 2009 .

[12]  Meshal Al-Shaher,et al.  Estimation of Fault Location and Fault Resistance for Single Line-to-ground Faults in Multi-ring Distribution Network Using Artificial Neural Network , 2009 .

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

[14]  L. L. Zhang,et al.  Morphology Singular Entropy-Based Phase Selector Using Short Data Window for Transmission Lines , 2014, IEEE Transactions on Power Delivery.

[15]  Pradipta Kishore Dash,et al.  Multiresolution S-transform-based fuzzy recognition system for power quality events , 2004 .

[16]  Arabinda Das,et al.  Transmission Line Fault Classification and Location Using Wavelet Entropy and Neural Network , 2012 .

[17]  Avinash Kumar Sinha,et al.  Application of wavelet multiresolution analysis for identification and classification of faults on transmission lines , 2005 .

[18]  Young Park,et al.  Novel technique for fault location estimation on parallel transmission lines using wavelet , 2007 .

[19]  Gevork B. Gharehpetian,et al.  Hyperbolic S-transform-based method for classification of external faults, incipient faults, inrush currents and internal faults in power transformers , 2012 .

[20]  Flavio B. Costa.  Fault-Induced Transient Detection Based on Real-Time Analysis of the Wavelet Coefficient Energy , 2014 .

[21]  Zaibin Jiao,et al.  A Novel Distance Protection Algorithm for the Phase-Ground Fault , 2014, IEEE Transactions on Power Delivery.

[22]  Yuan Liao,et al.  Accurate Transmission Line Fault Location Considering Shunt Capacitances Without Utilizing Line Parameters , 2011 .

[23]  P. K. Dashb,et al.  Fault classification and location using HS-transform and radial basis function neural network , 2006 .

[24]  Manoj Tripathy,et al.  Probabilistic neural-network-based protection of power transformer , 2007 .

[25]  O.P. Malik,et al.  High impedance fault detection based on wavelet transform and statistical pattern recognition , 2005, IEEE Transactions on Power Delivery.

[26]  Pradipta Kishore Dash,et al.  Estimation of time-varying power quality indices with an adaptive window-based fast generalised S-transform , 2012 .

[27]  Johan Driesen,et al.  Assessment of voltage sag indices based on scaling and wavelet coefficient energy analysis , 2013, 2013 IEEE Power & Energy Society General Meeting.

[28]  Majid Sanaye-Pasand,et al.  A New Approach Based on S-transform for Discrimination and Classification of Inrush Current from Internal Fault Currents Using Probabilistic Neural Network , 2010 .

[29]  Amany El-Zonkoly,et al.  Applying wavelet entropy principle in fault classification , 2008 .

[30]  Sukumar Mishra,et al.  Detection and classification of power quality disturbances using S-Transform and probabilistic neural network , 2009, 2009 IEEE/PES Power Systems Conference and Exposition.

[31]  N. Perera,et al.  Series-Compensated Double-Circuit Transmission- Line Protection Using Directions of Current Transients , 2013, IEEE Transactions on Power Delivery.

[32]  Lalu Mansinha,et al.  Localization of the complex spectrum: the S transform , 1996, IEEE Trans. Signal Process..

[33]  K. R. Krishnanand,et al.  A New Real-Time Fast Discrete S-Transform for Cross-Differential Protection of Shunt-Compensated Power Systems , 2013, IEEE Transactions on Power Delivery.

[34]  Almoataz Y. Abdelaziz,et al.  Fault Location in Long Transmission Lines Using Synchronized Phasor Measurements from Both Ends , 2012 .

[35]  A. Ahmadimanesh,et al.  Transient-Based Fault-Location Method for Multiterminal Lines Employing S-Transform , 2013, IEEE Transactions on Power Delivery.

[36]  Atthapol Ngaopitakkul,et al.  An application of a discrete wavelet transform and a back-propagation neural network algorithm for fault diagnosis on single-circuit transmission line , 2013, Int. J. Syst. Sci..

[37]  Garikoitz Buigues,et al.  High impedance fault detection methodology using wavelet transform and artificial neural networks , 2011 .

[38]  G. Panda,et al.  Power Quality Analysis Using S-Transform , 2002, IEEE Power Engineering Review.

[39]  Rahul Dubey,et al.  Spectral energy function for fault detection during power swing , 2011 .

[40]  Shaofeng Huang,et al.  A Novel Method of Ground Fault Phase Selection in Weak-Infeed Side , 2014, IEEE Transactions on Power Delivery.

[41]  Rengang Yang,et al.  Power-Quality Disturbance Recognition Using S-Transform , 2007, IEEE Transactions on Power Delivery.

[42]  Maryam Mirzaei,et al.  Comparative Analysis of Probabilistic Neural Network, Radial Basis Function, and Feed-forward Neural Network for Fault Classification in Power Distribution Systems , 2011 .

[43]  S.M. Rovnyak,et al.  Decision tree-based methodology for high impedance fault detection , 2004, IEEE Transactions on Power Delivery.

[44]  Adisa A. Jimoh,et al.  Fault location in a series compensated transmission line based on wavelet packet decomposition and s , 2011 .

[45]  M. Jayabharata Reddy,et al.  A wavelet-fuzzy combined approach for classification and location of transmission line faults , 2007 .

[46]  P. Dash,et al.  Transmission line distance relaying using a variable window short-time Fourier transform , 2008 .

[47]  Jinfang Zhang,et al.  Fault localization in electrical power systems: A pattern recognition approach , 2011 .

[48]  P. S. Bhowmik,et al.  A novel wavelet transform aided neural network based transmission line fault analysis method , 2009 .

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