Fault Detection and Classification Scheme for Transmission Lines Connecting Windfarm Using Single end Impedance

Abstract

This paper proposes a new algorithm for the detection and classification of faults in transmission lines in the presence of distributed generation. The proposed algorithm uses wavelet transform-based detail and approximation coefficients of voltage and current signals over one cycle after fault inception at one end of the transmission line. The line impedance calculated from the ratio of the voltage and current approximation coefficients is fed to the fuzzy inference system to classify faults. The proposed algorithm has been tested successfully in a real-time digital simulator for nonlinear high impedance faults in the presence of wind farm, considering the effects of CT saturation. All types of faults with the variation of pre-fault loading condition, line parameters, line length and source impedance can be correctly classified using the proposed algorithm.

Keywords:

• CT saturation
• Discrete wavelet transform
• Distributed generation
• Fuzzy inference system
• High impedance fault
• Impedance-based fault classification

Additional information

Notes on contributors

Monideepa Paul

Monideepa Paul received the MTech degree in electrical engineering from NIT Durgapur, India. She has a rich and diverse academic career as a faculty in electrical engineering. She is currently pursuing PhD degree in fault detection in transmission and distribution systems from Jadavpur University, Kolkata, India. Her research activities mainly focus on fault detection and classification and real-time simulations. E-mail: [email protected]

Sudipta Debnath

Sudipta Debnath received the BE, MTech, and PhD degree's from NIT Durgapur, Calcutta University, and IIEST Shibpur, respectively. Presently, she is working as professor with the Department of Electrical Engineering, Jadavpur University, Kolkata, India. Her current research interests include fault detection and power quality improvement in distribution systems.