NDLI: Efficient and robust ANN-based method for an improved protection of VSC-HVDC systems

Content Provider	IET Digital Library
Author	Merlin, Victor Luiz Santos, Ricardo Caneloi dos Blond, Simon Le Coury, Denis Vinicius
Abstract	This study presents a method based on an artificial neural network (ANN) for protecting voltage source converters (VSC)-high-voltage direct current (HVDC) systems. The proposed method takes advantage of ANNs’ excellent capabilities in pattern recognition and classification. The solution presented here is able to detect a fault condition in the whole HVDC system based only on voltage waveforms measured at the rectifier substation, which is an important advantage over other fault detection methods. For any given fault condition, the faulty zone is identified and then the fault is classified concerning the phases or poles involved. The proposed method is shown to be both robust against false operations and very reliable with respect to fault detection and classification. A large number of simulations were performed and results show the effectiveness of the proposed scheme. Different operational conditions and fault cases were considered to evaluate the proposed algorithm, ensuring excellent performance concerning a wide range of possible situations in VSC-HVDC systems.
Starting Page	1555
Ending Page	1562
Page Count	8
ISSN	17521416
Volume Number	12
e-ISSN	17521424
Issue Number	Issue 13, Oct (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rpg/12/13
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rpg.2018.5097
Journal	IET Renewable Power Generation
Publisher Date	2018-06-28
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Artificial Neural Network D.C. Transmission Fault Classification Fault Condition Detection Fault Detection Method Fault Diagnosis Faulty Zone HVDC Power Converter HVDC Power Transmission Improved VSC-HVDC System Protection Neural Computing Technique Neural Nets Pattern Classification Pattern Recognition Power Engineering Computing Power System Protection Power Transmission Fault Power Transmission Protection Rectifier Rectifier Substation Robust ANN-based Method Substations Voltage Source Converter-high-voltage Direct Current System Protection Voltage Waveform Voltage-source Converter
Content Type	Text
Resource Type	Article
Subject	Renewable Energy, Sustainability and the Environment

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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