Adaptive protection for series-compensated transmission lines using neural networks
Hosny, A., Safiuddin, M. (2008). Adaptive protection for series-compensated transmission lines using neural networks. EKB Journal Management System, 6(6th International Conference on Electrical Engineering ICEENG 2008), 1-13. doi: 10.21608/iceeng.2008.34526
A. Hosny; M. Safiuddin. "Adaptive protection for series-compensated transmission lines using neural networks". EKB Journal Management System, 6, 6th International Conference on Electrical Engineering ICEENG 2008, 2008, 1-13. doi: 10.21608/iceeng.2008.34526
Hosny, A., Safiuddin, M. (2008). 'Adaptive protection for series-compensated transmission lines using neural networks', EKB Journal Management System, 6(6th International Conference on Electrical Engineering ICEENG 2008), pp. 1-13. doi: 10.21608/iceeng.2008.34526
Hosny, A., Safiuddin, M. Adaptive protection for series-compensated transmission lines using neural networks. EKB Journal Management System, 2008; 6(6th International Conference on Electrical Engineering ICEENG 2008): 1-13. doi: 10.21608/iceeng.2008.34526

Adaptive protection for series-compensated transmission lines using neural networks

The International Conference on Electrical Engineering
Article 155, Volume 6, 6th International Conference on Electrical Engineering ICEENG 2008, May 2008, Pages 1-13    PDF (189.81 K)
Document Type: Original Article
DOI: 10.21608/iceeng.2008.34526
Authors
A. Hosny1; M. Safiuddin2
1Student member, IEEE., A. Hosny is a Doctoral Candidate at the State University of New York at Buffalo, Amherst, NY 14260 USA. Phone: 716 645 3115 ext. 1204; Fax: 716 645 365.
2Fellow, IEEE., M. Safiuddin is with the Department of Electrical Engineering, University at Buffalo, Amherst, NY 14260 USA.
Abstract
Abstract:
This paper presents an adaptive protection approach for classifying and locating faults
in Thyristor Controlled Series-Compensated (TCSC) transmission lines. The proposed
scheme is based on Multilayer Feedforward Neural Networks (MFNNs). Levenberg-
Marquardt (LM) training algorithm is employed. The LM algorithm appears to be the
fastest training algorithm and highly nominated for better generalized models. Threephase
power system currents and voltages at the relay location are used as inputs to
MFNN-based relay. Two neural networks are trained to address fault classification and
location. Feasibility and reliability of the proposed scheme are investigated using fault
data set of a typical 500 kV power system simulated in EMTP-ATP package. Studied
system is subjected to all possible shunt faults at different operating conditions,
including fault location, fault inception angle and fault resistance. Simulation results
demonstrate that MFNN-based relay system is very robust, fault tolerant, and highly
accurate in protecting Flexible AC Transmission Systems (FACTS), such as
transmission lines with TCSC.
Keywords
Fault classification; fault location; Back-Propagation Neural Networks (BPNN); Thyristor-Controlled Series Compensated (TCSC) Transmission Lines; FACTS
Statistics
Article View: 154
PDF Download: 126