NDLI: Accurate fault location and faulted section determination based on deep learning for a parallel-compensated three-terminal transmission line

Content Provider	IET Digital Library
Author	Mirzaei, Mahdi Vahidi, Behrooz Hosseini, Seyed Hossein
Abstract	Parallel flexible AC transmission systems (FACTS) devices affect the performance of protection relays and conventional phasor-based fault location schemes in transmission lines. This study focuses on both multi-terminal and parallel-compensated lines, not investigated simultaneously in previous works. An algorithm based on deep neural networks is proposed for fault location in a three-terminal transmission line with the presence of parallel FACTS device. The line model and fault occurrence are simulated in SIMULINK and features are extracted from voltages at the three terminals by wavelet transform. The generated features are used to train a deep neural network which determines faulted line section and fault distance simultaneously. The adopted intelligence-based approach has the advantage of not requiring pre-knowledge of line specifications, FACTS devices modelling and the uncertainty in compensator parameters. A large number of fault scenarios are investigated. The faulted section is recognised correctly in 100% of test cases. The algorithm performance is acceptable for both symmetrical and unsymmetrical fault types, small fault inception angles and high fault resistance. The accuracy of fault location is improved compared to previous schemes (total mean error of 0.0993%). The proposed algorithm provides an accurate, fast and robust tool for fault location in parallel-compensated three-terminal transmission lines.
Starting Page	2770
Ending Page	2778
Page Count	9
ISSN	17518687
Volume Number	13
e-ISSN	17518695
Issue Number	Issue 13, Jul (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-gtd/13/13
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-gtd.2018.6982
Journal	IET Generation, Transmission & Distribution
Publisher Date	2019-04-24
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	A.c. Transmission Control of Electric Power System Deep Neural Network Fault Distance Fault Inception Angles Fault Location Fault Occurrence Fault Resistance Faulted Line Section Faulted Section Determination Flexible AC Transmission System Integral Transforms Intelligence-based Approach Knowledge Engineering Technique Learning in AI Line Model Multiterminal Line Neural Computing Technique Neural Nets Parallel FACTS Device Parallel Flexible AC Transmission System Parallel-compensated Line Phasor-based Fault Location Scheme Power Engineering Computing Power System Control Power System Protection Power Transmission Cables Power Transmission Control Power Transmission Fault Power Transmission Line Power Transmission Protection Power Transmission Reliability Power Transmission System Relay Protection Reliability SIMULINK Three-terminal Transmission Line Unsymmetrical Fault Types Wavelet Transform
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Energy Engineering and Power Technology Electrical and Electronic Engineering

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