Design, implementation and testing of an artificial neural network based fault direction discriminator for protecting transmission lines

Journal Article · Sat Apr 01 04:00:00 EST 1995 · IEEE Transactions on Power Delivery

DOI:https://doi.org/10.1109/61.400862· OSTI ID:64414

Sidhu, T S; Singh, H; Sachdev, M S ^[1]

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1. Univ. of Saskatchewan, Saskatoon (Canada). Power Systems Research Group

This paper describes a fault direction discriminator that uses an Artificial Neural Network (ANN) for protecting transmission lines. The discriminator uses various attributes to reach a decision and tends to emulate the conventional pattern classification problem. An equation of the boundary describing the classification is embedded in the Multilayer Feedforward Neural Network (MFNN) by training through the use of an appropriate learning algorithm and suitable training data. The discriminator uses instantaneous values of the line voltages and line currents to make decisions. Results showing the performance of the ANN-based discriminator are presented in the paper and indicate that it is fast, robust and accurate. It is suitable for realizing an ultrafast directional comparison protection of transmission lines.

• OSTI ID: 64414
• Report Number(s): CONF-940702--
• Journal Information: IEEE Transactions on Power Delivery, Journal Name: IEEE Transactions on Power Delivery Journal Issue: 2 Vol. 10; ISSN 0885-8977; ISSN ITPDE5
• Country of Publication: United States
• Language: English

Related Subjects

24 POWER TRANSMISSION AND DISTRIBUTION
ARTIFICIAL INTELLIGENCE
EHV AC SYSTEMS
EQUIPMENT PROTECTION DEVICES
HVAC SYSTEMS
NEURAL NETWORKS
POWER TRANSMISSION LINES