Fast parameter identification and modeling of electric load based on simplified composite load model

Songtai Yu; Shuqing Zhang; Yingduo Han; Chao Lu; Zhihong Yu; Xinran Zhang

doi:10.1109/PESGM.2015.7286063

Fast parameter identification and modeling of electric load based on simplified composite load model

Songtai Yu
, Shuqing Zhang
, Yingduo Han
, Chao Lu
, Zhihong Yu
, Xinran Zhang

Tsinghua University
Chinese Electric Power Research Institute

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

9 Scopus citations

Abstract

Load is one of the most important parts of power system and imposes great impact on stability of the system. Proper modeling and parameter identification of load are instructive for power system stability study and control. However, because of the nonlinearity, randomness and time-variation, load modeling and parameter identification remain a big problem in power system simulation and stability analysis. Composite load model is one of the most common models for load in power system simulation. This paper introduces an online parameter identification and modeling method for electric load based on a new simplified composite load model. The approach includes three parts: the dominance analysis of parameter and model simplification, the phasor measurement and calculation, and the online parameter identification. A test on the method is also shown with the simulation of a typical case system.

Original language	English
Title of host publication	2015 IEEE Power and Energy Society General Meeting, PESGM 2015
Publisher	IEEE Computer Society
ISBN (Electronic)	9781467380409
DOIs	https://doi.org/10.1109/PESGM.2015.7286063
State	Published - 30 Sep 2015
Externally published	Yes
Event	IEEE Power and Energy Society General Meeting, PESGM 2015 - Denver, United States Duration: 26 Jul 2015 → 30 Jul 2015

Publication series

Name	IEEE Power and Energy Society General Meeting
Volume	2015-September
ISSN (Print)	1944-9925
ISSN (Electronic)	1944-9933

Conference

Conference	IEEE Power and Energy Society General Meeting, PESGM 2015
Country/Territory	United States
City	Denver
Period	26/07/15 → 30/07/15

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

PAF method
STP filter
electric load
model simplification
parameter identification

Access to Document

10.1109/PESGM.2015.7286063

Cite this

Yu, S., Zhang, S., Han, Y., Lu, C., Yu, Z., & Zhang, X. (2015). Fast parameter identification and modeling of electric load based on simplified composite load model. In 2015 IEEE Power and Energy Society General Meeting, PESGM 2015 Article 7286063 (IEEE Power and Energy Society General Meeting; Vol. 2015-September). IEEE Computer Society. https://doi.org/10.1109/PESGM.2015.7286063

@inproceedings{cbe95cd8d02942b2a74247270d202880,

title = "Fast parameter identification and modeling of electric load based on simplified composite load model",

abstract = "Load is one of the most important parts of power system and imposes great impact on stability of the system. Proper modeling and parameter identification of load are instructive for power system stability study and control. However, because of the nonlinearity, randomness and time-variation, load modeling and parameter identification remain a big problem in power system simulation and stability analysis. Composite load model is one of the most common models for load in power system simulation. This paper introduces an online parameter identification and modeling method for electric load based on a new simplified composite load model. The approach includes three parts: the dominance analysis of parameter and model simplification, the phasor measurement and calculation, and the online parameter identification. A test on the method is also shown with the simulation of a typical case system.",

keywords = "PAF method, STP filter, electric load, model simplification, parameter identification",

author = "Songtai Yu and Shuqing Zhang and Yingduo Han and Chao Lu and Zhihong Yu and Xinran Zhang",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE Power and Energy Society General Meeting, PESGM 2015 ; Conference date: 26-07-2015 Through 30-07-2015",

year = "2015",

month = sep,

day = "30",

doi = "10.1109/PESGM.2015.7286063",

language = "英语",

series = "IEEE Power and Energy Society General Meeting",

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booktitle = "2015 IEEE Power and Energy Society General Meeting, PESGM 2015",

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}

Yu, S, Zhang, S, Han, Y, Lu, C, Yu, Z & Zhang, X 2015, Fast parameter identification and modeling of electric load based on simplified composite load model. in 2015 IEEE Power and Energy Society General Meeting, PESGM 2015., 7286063, IEEE Power and Energy Society General Meeting, vol. 2015-September, IEEE Computer Society, IEEE Power and Energy Society General Meeting, PESGM 2015, Denver, United States, 26/07/15. https://doi.org/10.1109/PESGM.2015.7286063

Fast parameter identification and modeling of electric load based on simplified composite load model. / Yu, Songtai; Zhang, Shuqing; Han, Yingduo et al.
2015 IEEE Power and Energy Society General Meeting, PESGM 2015. IEEE Computer Society, 2015. 7286063 (IEEE Power and Energy Society General Meeting; Vol. 2015-September).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Fast parameter identification and modeling of electric load based on simplified composite load model

AU - Yu, Songtai

AU - Zhang, Shuqing

AU - Han, Yingduo

AU - Lu, Chao

AU - Yu, Zhihong

AU - Zhang, Xinran

PY - 2015/9/30

Y1 - 2015/9/30

N2 - Load is one of the most important parts of power system and imposes great impact on stability of the system. Proper modeling and parameter identification of load are instructive for power system stability study and control. However, because of the nonlinearity, randomness and time-variation, load modeling and parameter identification remain a big problem in power system simulation and stability analysis. Composite load model is one of the most common models for load in power system simulation. This paper introduces an online parameter identification and modeling method for electric load based on a new simplified composite load model. The approach includes three parts: the dominance analysis of parameter and model simplification, the phasor measurement and calculation, and the online parameter identification. A test on the method is also shown with the simulation of a typical case system.

AB - Load is one of the most important parts of power system and imposes great impact on stability of the system. Proper modeling and parameter identification of load are instructive for power system stability study and control. However, because of the nonlinearity, randomness and time-variation, load modeling and parameter identification remain a big problem in power system simulation and stability analysis. Composite load model is one of the most common models for load in power system simulation. This paper introduces an online parameter identification and modeling method for electric load based on a new simplified composite load model. The approach includes three parts: the dominance analysis of parameter and model simplification, the phasor measurement and calculation, and the online parameter identification. A test on the method is also shown with the simulation of a typical case system.

KW - PAF method

KW - STP filter

KW - electric load

KW - model simplification

KW - parameter identification

UR - https://www.scopus.com/pages/publications/84956862769

U2 - 10.1109/PESGM.2015.7286063

DO - 10.1109/PESGM.2015.7286063

M3 - 会议稿件

AN - SCOPUS:84956862769

T3 - IEEE Power and Energy Society General Meeting

BT - 2015 IEEE Power and Energy Society General Meeting, PESGM 2015

PB - IEEE Computer Society

T2 - IEEE Power and Energy Society General Meeting, PESGM 2015

Y2 - 26 July 2015 through 30 July 2015

ER -

Fast parameter identification and modeling of electric load based on simplified composite load model

Abstract

Publication series

Conference

UN SDGs

Keywords

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