Online parameter identification and generic modeling derivation of a dynamic load model in distribution grids

Theofilos A. Papadopoulos; Georgios A. Barzegkar-Ntovom; Vassilis C. Nikolaidis; Panagiotis N. Papadopoulos; Graeme M. Burt

doi:10.1109/PTC.2017.7980994

Online parameter identification and generic modeling derivation of a dynamic load model in distribution grids

Theofilos A. Papadopoulos, Georgios A. Barzegkar-Ntovom, Vassilis C. Nikolaidis, Panagiotis N. Papadopoulos, Graeme M. Burt

Electronic And Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

10 Citations (Scopus)

Abstract

The advent of smart grids and the installation of phasor measurement units in the distribution network have renewed the interest on the measurement-based load modeling approach. In this paper, a real-time load modeling and identification procedure of the well-known exponential recovery dynamic load model using synchrophasor data is presented. The performance of the proposed method is evaluated using measurements recorded in a low-voltage laboratory scale test rig. Several parameters of the procedure are investigated to evaluate the applicability of the method under real world conditions, including the impact of filtering techniques, outlier rejection, model optimization algorithms, etc. The findings of this paper verify the validity of the proposed method for realtime applications.

Original language	English
Title of host publication	2017 IEEE Manchester PowerTech
Place of Publication	Piscataway, NJ
Publisher	IEEE
Number of pages	6
ISBN (Print)	9781509042388, 9781509042371
DOIs	https://doi.org/10.1109/PTC.2017.7980994
Publication status	Published - 20 Jul 2017
Event	2017 IEEE Manchester PowerTech, Powertech 2017 - Manchester, United Kingdom Duration: 18 Jun 2017 → 22 Jun 2017

Conference

Conference	2017 IEEE Manchester PowerTech, Powertech 2017
Country/Territory	United Kingdom
City	Manchester
Period	18/06/17 → 22/06/17

Keywords

dynamic load modeling
exponential recovery model
measurement-based approach
nonlinear least-square optimization
real-time identification

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10.1109/PTC.2017.7980994

Cite this

@inproceedings{b3b074415d714c6b821501e0d11aacd6,

title = "Online parameter identification and generic modeling derivation of a dynamic load model in distribution grids",

abstract = "The advent of smart grids and the installation of phasor measurement units in the distribution network have renewed the interest on the measurement-based load modeling approach. In this paper, a real-time load modeling and identification procedure of the well-known exponential recovery dynamic load model using synchrophasor data is presented. The performance of the proposed method is evaluated using measurements recorded in a low-voltage laboratory scale test rig. Several parameters of the procedure are investigated to evaluate the applicability of the method under real world conditions, including the impact of filtering techniques, outlier rejection, model optimization algorithms, etc. The findings of this paper verify the validity of the proposed method for realtime applications.",

keywords = "dynamic load modeling, exponential recovery model, measurement-based approach, nonlinear least-square optimization, real-time identification",

author = "Papadopoulos, {Theofilos A.} and Barzegkar-Ntovom, {Georgios A.} and Nikolaidis, {Vassilis C.} and Papadopoulos, {Panagiotis N.} and Burt, {Graeme M.}",

year = "2017",

month = jul,

day = "20",

doi = "10.1109/PTC.2017.7980994",

language = "English",

isbn = "9781509042388",

booktitle = "2017 IEEE Manchester PowerTech",

publisher = "IEEE",

note = "2017 IEEE Manchester PowerTech, Powertech 2017 ; Conference date: 18-06-2017 Through 22-06-2017",

}

Papadopoulos, TA, Barzegkar-Ntovom, GA, Nikolaidis, VC, Papadopoulos, PN & Burt, GM 2017, Online parameter identification and generic modeling derivation of a dynamic load model in distribution grids. in 2017 IEEE Manchester PowerTech., 7980994, IEEE, Piscataway, NJ, 2017 IEEE Manchester PowerTech, Powertech 2017, Manchester, United Kingdom, 18/06/17. https://doi.org/10.1109/PTC.2017.7980994

TY - GEN

T1 - Online parameter identification and generic modeling derivation of a dynamic load model in distribution grids

AU - Papadopoulos, Theofilos A.

AU - Barzegkar-Ntovom, Georgios A.

AU - Nikolaidis, Vassilis C.

AU - Papadopoulos, Panagiotis N.

AU - Burt, Graeme M.

PY - 2017/7/20

Y1 - 2017/7/20

N2 - The advent of smart grids and the installation of phasor measurement units in the distribution network have renewed the interest on the measurement-based load modeling approach. In this paper, a real-time load modeling and identification procedure of the well-known exponential recovery dynamic load model using synchrophasor data is presented. The performance of the proposed method is evaluated using measurements recorded in a low-voltage laboratory scale test rig. Several parameters of the procedure are investigated to evaluate the applicability of the method under real world conditions, including the impact of filtering techniques, outlier rejection, model optimization algorithms, etc. The findings of this paper verify the validity of the proposed method for realtime applications.

AB - The advent of smart grids and the installation of phasor measurement units in the distribution network have renewed the interest on the measurement-based load modeling approach. In this paper, a real-time load modeling and identification procedure of the well-known exponential recovery dynamic load model using synchrophasor data is presented. The performance of the proposed method is evaluated using measurements recorded in a low-voltage laboratory scale test rig. Several parameters of the procedure are investigated to evaluate the applicability of the method under real world conditions, including the impact of filtering techniques, outlier rejection, model optimization algorithms, etc. The findings of this paper verify the validity of the proposed method for realtime applications.

KW - dynamic load modeling

KW - exponential recovery model

KW - measurement-based approach

KW - nonlinear least-square optimization

KW - real-time identification

UR - http://www.scopus.com/inward/record.url?scp=85034745216&partnerID=8YFLogxK

U2 - 10.1109/PTC.2017.7980994

DO - 10.1109/PTC.2017.7980994

M3 - Conference contribution book

AN - SCOPUS:85034745216

SN - 9781509042388

SN - 9781509042371

BT - 2017 IEEE Manchester PowerTech

PB - IEEE

CY - Piscataway, NJ

T2 - 2017 IEEE Manchester PowerTech, Powertech 2017

Y2 - 18 June 2017 through 22 June 2017

ER -

Online parameter identification and generic modeling derivation of a dynamic load model in distribution grids

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