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, Page 1-13 PDF (189.81 K) | ||||
Document Type: Original Article | ||||
DOI: 10.21608/iceeng.2008.34526 | ||||
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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 | ||||
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