Abstract
In this paper we inspect support vector regression (SVR) based fault position in a TCSC (thyristor controlled series capacitor) based long transmission line. This technique uses 1 cycle post faulty current signal from the transmission line and decomposed by wavelet packet transform. From the decomposed signal entropy and energy are extracted and fed to the forward feature selection method to eliminate the redundant data set. Then optimal future data set is normalized. Taking different simulation situation like fault type, resistance path, inception angle, and distance train and test data are produced. By using particle swarm optimization technique SVR parameters are optimized. Then normalized data set is fed to SVR to locate the fault position in TCSC based long transmission line. It is noticed that fault position error is less, than 0.29 percentages.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.