Development of a Novel Positive Sequence Contactor Model Using Deep Neural Networks

Sameer Nekkalapu; Vijay Vittal; John Undrill; Bo Gong; Kenneth Brown

doi:10.1109/TPWRS.2023.3296096

Development of a Novel Positive Sequence Contactor Model Using Deep Neural Networks

Sameer Nekkalapu, Vijay Vittal, John Undrill, Bo Gong, Kenneth Brown

Engineering, Ira A. Fulton Schools of (IAFSE)

Research output: Contribution to journal › Article › peer-review

Abstract

In this work, a novel positive sequence (PS) contactor model for the protection of the ‘motorc’ model [1] has been developed using a detailed novel methodology, using linear regression and deep neural networks (DNNs). This model has been developed to identify and incorporate the critical tripping and reconnection characteristics of a newly developed 24 V electromagnetic transient (EMT) contactor [2] from an EMT simulator (PSCAD) into a positive sequence simulator (GE PSLFTM). The impact of the accurate EMT contactor model on the motor stalling phenomenon, to accurately estimate fault induced delayed voltage recovery (FIDVR) type phenomenon for single-line to ground (SLG) faults, is also accurately captured using the proposed methodology.

Original language	English (US)
Pages (from-to)	1-15
Number of pages	15
Journal	IEEE Transactions on Power Systems
DOIs	https://doi.org/10.1109/TPWRS.2023.3296096
State	Accepted/In press - 2023

Keywords

Analytical models
Behavioral sciences
Circuit faults
Computational modeling
Contacts
DNNs
EMT
FIDVR
GE PSLFTM
Load modeling
Mathematical models
POW
PSCAD
SPHIMS
contactors
load modeling
motors
regression
stalling

ASJC Scopus subject areas

Energy Engineering and Power Technology
Electrical and Electronic Engineering

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10.1109/TPWRS.2023.3296096

Cite this

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title = "Development of a Novel Positive Sequence Contactor Model Using Deep Neural Networks",

abstract = "In this work, a novel positive sequence (PS) contactor model for the protection of the {\textquoteleft}motorc{\textquoteright} model [1] has been developed using a detailed novel methodology, using linear regression and deep neural networks (DNNs). This model has been developed to identify and incorporate the critical tripping and reconnection characteristics of a newly developed 24 V electromagnetic transient (EMT) contactor [2] from an EMT simulator (PSCAD) into a positive sequence simulator (GE PSLFTM). The impact of the accurate EMT contactor model on the motor stalling phenomenon, to accurately estimate fault induced delayed voltage recovery (FIDVR) type phenomenon for single-line to ground (SLG) faults, is also accurately captured using the proposed methodology.",

keywords = "Analytical models, Behavioral sciences, Circuit faults, Computational modeling, Contacts, DNNs, EMT, FIDVR, GE PSLFTM, Load modeling, Mathematical models, POW, PSCAD, SPHIMS, contactors, load modeling, motors, regression, stalling",

author = "Sameer Nekkalapu and Vijay Vittal and John Undrill and Bo Gong and Kenneth Brown",

note = "Publisher Copyright: IEEE",

year = "2023",

doi = "10.1109/TPWRS.2023.3296096",

language = "English (US)",

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journal = "IEEE Transactions on Power Systems",

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AU - Nekkalapu, Sameer

AU - Vittal, Vijay

AU - Undrill, John

AU - Gong, Bo

AU - Brown, Kenneth

N1 - Publisher Copyright: IEEE

PY - 2023

Y1 - 2023

N2 - In this work, a novel positive sequence (PS) contactor model for the protection of the ‘motorc’ model [1] has been developed using a detailed novel methodology, using linear regression and deep neural networks (DNNs). This model has been developed to identify and incorporate the critical tripping and reconnection characteristics of a newly developed 24 V electromagnetic transient (EMT) contactor [2] from an EMT simulator (PSCAD) into a positive sequence simulator (GE PSLFTM). The impact of the accurate EMT contactor model on the motor stalling phenomenon, to accurately estimate fault induced delayed voltage recovery (FIDVR) type phenomenon for single-line to ground (SLG) faults, is also accurately captured using the proposed methodology.

AB - In this work, a novel positive sequence (PS) contactor model for the protection of the ‘motorc’ model [1] has been developed using a detailed novel methodology, using linear regression and deep neural networks (DNNs). This model has been developed to identify and incorporate the critical tripping and reconnection characteristics of a newly developed 24 V electromagnetic transient (EMT) contactor [2] from an EMT simulator (PSCAD) into a positive sequence simulator (GE PSLFTM). The impact of the accurate EMT contactor model on the motor stalling phenomenon, to accurately estimate fault induced delayed voltage recovery (FIDVR) type phenomenon for single-line to ground (SLG) faults, is also accurately captured using the proposed methodology.

KW - Analytical models

KW - Behavioral sciences

KW - Circuit faults

KW - Computational modeling

KW - Contacts

KW - DNNs

KW - EMT

KW - FIDVR

KW - GE PSLFTM

KW - Load modeling

KW - Mathematical models

KW - POW

KW - PSCAD

KW - SPHIMS

KW - contactors

KW - load modeling

KW - motors

KW - regression

KW - stalling

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EP - 15

JO - IEEE Transactions on Power Systems

JF - IEEE Transactions on Power Systems

ER -

Development of a Novel Positive Sequence Contactor Model Using Deep Neural Networks

Abstract

Keywords

ASJC Scopus subject areas

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