• DocumentCode
    586807
  • Title

    New method for fault classification in TCSC compensated transmission line using GA tuned SVM

  • Author

    Tripathi, Priyanka ; Pillai, G.N. ; Gupta, H.O.

  • Author_Institution
    Dept. of Electr. Eng., Indian Inst. of Technol. Roorkee, Roorkee, India
  • fYear
    2012
  • fDate
    Oct. 30 2012-Nov. 2 2012
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    Presence of TCSC (Thyristor-Controlled Series Compensator) compensated transmission lines is increasing in modern power systems due to their benefits like increased power flow capacity but these benefits come at the cost of difficulty in protection of the transmission line. This paper presents a new method using SVM (Support Vector Machine) for fault classification in such line. This method is compared with existing SVM based methods and higher classification accuracy has been achieved. The improved accuracy is achieved by changing the architecture and input of the classifier. Genetic Algorithm (GA) is used to search globally optimum value of SVM parameters. Effect of sampling frequency and data window length on proposed scheme is also analyzed.
  • Keywords
    fault diagnosis; genetic algorithms; power engineering computing; power transmission protection; support vector machines; GA tuned SVM; TCSC compensated transmission line; fault classification; genetic algorithm; power flow capacity; power systems; support vector machine; thyristor-controlled series compensator; transmission line protection; Accuracy; Genetic algorithms; Power capacitors; Power transmission lines; Support vector machines; Thyristors; Training; Fault Classification; Genetic Algorithm (GA); Support Vector Machine (SVM); Thyristor-Controlled Series Compensator (TCSC);
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power System Technology (POWERCON), 2012 IEEE International Conference on
  • Conference_Location
    Auckland
  • Print_ISBN
    978-1-4673-2868-5
  • Type

    conf

  • DOI
    10.1109/PowerCon.2012.6401382
  • Filename
    6401382