New Pattern-Recognition Method for Fault Analysis in Transmission Line With UPFC

In this paper, a new set of time-frequency features for fault-type identification, fault-loop status supervision, and fault-zone detection modules in a compensated transmission line with a unified power-flow controller is proposed. Some features are extracted from a one-cycle data window of one side of the compensated line, including 3/16 cycle of postfault data by the fast discrete orthonormal S-Transform (FDOST). The computation burden of the FDOST as a time-frequency decomposition is the same as the fast Fourier transform. The support vector machine is employed for classification of the ranked features by the Gram-Schmidt method. The graphical representations of extracted features and the obtained numerical results under different conditions confirm the efficacy of the proposed scheme.

[1]  Nello Cristianini,et al.  Support vector and kernel machines , 2001 .

[2]  A.P. Apostolov,et al.  Superimposed components based sub-cycle protection of transmission lines , 2004, IEEE PES Power Systems Conference and Exposition, 2004..

[3]  M. Pazoki,et al.  Impact of UPFC on Power Swing Characteristic and Distance Relay Behavior , 2014, IEEE Transactions on Power Delivery.

[4]  R. Brereton,et al.  Support vector machines for classification and regression. , 2010, The Analyst.

[5]  T.S. Sidhu,et al.  Performance Comparison of Distance Protection Schemes for Shunt-FACTS Compensated Transmission Lines , 2007, IEEE Transactions on Power Delivery.

[6]  A. Salemnia,et al.  Impact of SSSC on the digital distance relaying , 2009, 2009 IEEE Power & Energy Society General Meeting.

[7]  Robert Glenn Stockwell,et al.  A basis for efficient representation of the S-transform , 2007, Digit. Signal Process..

[8]  Isabelle Guyon,et al.  Practical Feature Selection: from Correlation to Causality , 2007, NATO ASI Mining Massive Data Sets for Security.

[9]  M. Khederzadeh,et al.  Impact of VSC-Based Multiline FACTS Controllers on Distance Protection of Transmission Lines , 2012, IEEE Transactions on Power Delivery.

[10]  Laszlo Gyugyi,et al.  Operation of the unified power flow controller (UPFC) under practical constraints , 1998 .

[11]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[12]  Khushboo Sahu Fuzzy Logic Based Fault Classification Scheme for Digital Distance Protection , 2017 .

[13]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[14]  Masoud Nikravesh,et al.  Feature Extraction - Foundations and Applications , 2006, Feature Extraction.

[15]  Joachim Diederich,et al.  Rule Extraction from Support Vector Machines , 2008, Studies in Computational Intelligence.

[16]  Subhransu Ranjan Samantaray A Data-Mining Model for Protection of FACTS-Based Transmission Line , 2013 .

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

[18]  Pradipta Kishore Dash,et al.  Pattern recognition based digital relaying for advanced series compensated line , 2008 .

[19]  Mojtaba Khederzadeh,et al.  STATCOM modeling impacts on performance evaluation of distance protection of transmission lines , 2011 .

[20]  Rudra Prakash Maheshwari,et al.  Improved fault analysis technique for protection of Thyristor controlled series compensated transmission line , 2014 .

[21]  Chul-Hwan Kim,et al.  Analysis of Impact of UPFC on Single Pole Auto Reclosures , 2009 .

[22]  Babak Mozafari,et al.  Digital distance protection of transmission lines in the presence of SSSC , 2012 .

[23]  Ying-Tung Hsiao,et al.  A Hybrid Framework for Fault Detection, Classification, and Location—Part II: Implementation and Test Results , 2011, IEEE Transactions on Power Delivery.

[24]  R. Suganya,et al.  Data Mining Concepts and Techniques , 2010 .

[25]  Laszlo Gyugyi,et al.  Understanding FACTS: Concepts and Technology of Flexible AC Transmission Systems , 1999 .

[26]  G. Panda,et al.  Fault Classification and Section Identification of an Advanced Series-Compensated Transmission Line Using Support Vector Machine , 2007, IEEE Transactions on Power Delivery.

[27]  Xu Zhang,et al.  Fault phase selection scheme of EHV/UHV transmission line protection for high-resistance faults , 2012 .

[28]  Chih-Wen Liu,et al.  Closure on "A new protection scheme for fault detection, direction discrimination, classification, and location in transmission lines" , 2003 .

[29]  Huseyin Eristi,et al.  Fault diagnosis system for series compensated transmission line based on wavelet transform and adaptive neuro-fuzzy inference system , 2013 .

[30]  Phil Beaumont,et al.  Correction to "Performance Evaluation of a Distance Relay as Applied to a Transmission System with UPFC" , 2007 .

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

[32]  Ying-Tung Hsiao,et al.  A Hybrid Framework for Fault Detection, Classification, and Location—Part I: Concept, Structure, and Methodology , 2011, IEEE Transactions on Power Delivery.

[33]  Mojtaba Khederzadeh The impact of FACTS device on digital multifunctional protective relays , 2002, IEEE/PES Transmission and Distribution Conference and Exhibition.

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

[35]  Huisheng Wang,et al.  Fuzzy-neuro approach to fault classification for transmission line protection , 1998 .

[36]  Bogdan Kasztenny,et al.  Phase Selection for Single-Pole Tripping: Weak Infeed Conditions and Cross-Country Faults , 2002 .

[37]  Raj Aggarwal,et al.  A complete scheme for fault detection, classification and location in transmission lines using neural networks , 2001 .