Identifiability Analysis of Load Model Parameter Identification with Likelihood Profile Method

Xinran Zhang; Chao Lu; Ying Wang

doi:10.1109/PESGM.2018.8586673

Identifiability Analysis of Load Model Parameter Identification with Likelihood Profile Method

Xinran Zhang
, Chao Lu
, Ying Wang

The University of Hong Kong
Tsinghua University

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

3 Scopus citations

Abstract

Load model parameter identification from practical measured data has become an essential method to build load models for power system simulation, analysis and control. With different power system practical measurement and operation conditions, which may include disturbance magnitudes, measurement errors and data lengths, the difficulty to identify load model parameters is also different, which would lead to the problem of practical identifiability. In this paper, a likelihood profile based parameter practical identifiability analysis method for load model identification is proposed. The load model structure and parameters used for identification and the method to identify parameters based on ambient signal are introduced first. The definition of identifiability together with the likelihood profile analysis method are then proposed, after which the procedures of load model parameter identifiability are given. Simulation is conducted in WSCC 9 bus system to show the effectiveness of the proposed method. Impact factors of load model parameter identifiability are also analyzed and simulated.

Original language	English
Title of host publication	2018 IEEE Power and Energy Society General Meeting, PESGM 2018
Publisher	IEEE Computer Society
ISBN (Electronic)	9781538677032
DOIs	https://doi.org/10.1109/PESGM.2018.8586673
State	Published - 21 Dec 2018
Externally published	Yes
Event	2018 IEEE Power and Energy Society General Meeting, PESGM 2018 - Portland, United States Duration: 5 Aug 2018 → 10 Aug 2018

Publication series

Name	IEEE Power and Energy Society General Meeting
Volume	2018-August
ISSN (Print)	1944-9925
ISSN (Electronic)	1944-9933

Conference

Conference	2018 IEEE Power and Energy Society General Meeting, PESGM 2018
Country/Territory	United States
City	Portland
Period	5/08/18 → 10/08/18

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

Confidential interval
Likelihood profile
Load model identification
Practical parameter identifiability

Access to Document

10.1109/PESGM.2018.8586673

Cite this

@inproceedings{0609319ff25e44e1b276e3d81f761591,

title = "Identifiability Analysis of Load Model Parameter Identification with Likelihood Profile Method",

abstract = "Load model parameter identification from practical measured data has become an essential method to build load models for power system simulation, analysis and control. With different power system practical measurement and operation conditions, which may include disturbance magnitudes, measurement errors and data lengths, the difficulty to identify load model parameters is also different, which would lead to the problem of practical identifiability. In this paper, a likelihood profile based parameter practical identifiability analysis method for load model identification is proposed. The load model structure and parameters used for identification and the method to identify parameters based on ambient signal are introduced first. The definition of identifiability together with the likelihood profile analysis method are then proposed, after which the procedures of load model parameter identifiability are given. Simulation is conducted in WSCC 9 bus system to show the effectiveness of the proposed method. Impact factors of load model parameter identifiability are also analyzed and simulated.",

keywords = "Confidential interval, Likelihood profile, Load model identification, Practical parameter identifiability",

author = "Xinran Zhang and Chao Lu and Ying Wang",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE Power and Energy Society General Meeting, PESGM 2018 ; Conference date: 05-08-2018 Through 10-08-2018",

year = "2018",

month = dec,

day = "21",

doi = "10.1109/PESGM.2018.8586673",

language = "英语",

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

publisher = "IEEE Computer Society",

booktitle = "2018 IEEE Power and Energy Society General Meeting, PESGM 2018",

address = "美国",

}

Zhang, X, Lu, C & Wang, Y 2018, Identifiability Analysis of Load Model Parameter Identification with Likelihood Profile Method. in 2018 IEEE Power and Energy Society General Meeting, PESGM 2018., 8586673, IEEE Power and Energy Society General Meeting, vol. 2018-August, IEEE Computer Society, 2018 IEEE Power and Energy Society General Meeting, PESGM 2018, Portland, United States, 5/08/18. https://doi.org/10.1109/PESGM.2018.8586673

Identifiability Analysis of Load Model Parameter Identification with Likelihood Profile Method. / Zhang, Xinran; Lu, Chao; Wang, Ying.
2018 IEEE Power and Energy Society General Meeting, PESGM 2018. IEEE Computer Society, 2018. 8586673 (IEEE Power and Energy Society General Meeting; Vol. 2018-August).

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

TY - GEN

T1 - Identifiability Analysis of Load Model Parameter Identification with Likelihood Profile Method

AU - Zhang, Xinran

AU - Lu, Chao

AU - Wang, Ying

PY - 2018/12/21

Y1 - 2018/12/21

N2 - Load model parameter identification from practical measured data has become an essential method to build load models for power system simulation, analysis and control. With different power system practical measurement and operation conditions, which may include disturbance magnitudes, measurement errors and data lengths, the difficulty to identify load model parameters is also different, which would lead to the problem of practical identifiability. In this paper, a likelihood profile based parameter practical identifiability analysis method for load model identification is proposed. The load model structure and parameters used for identification and the method to identify parameters based on ambient signal are introduced first. The definition of identifiability together with the likelihood profile analysis method are then proposed, after which the procedures of load model parameter identifiability are given. Simulation is conducted in WSCC 9 bus system to show the effectiveness of the proposed method. Impact factors of load model parameter identifiability are also analyzed and simulated.

AB - Load model parameter identification from practical measured data has become an essential method to build load models for power system simulation, analysis and control. With different power system practical measurement and operation conditions, which may include disturbance magnitudes, measurement errors and data lengths, the difficulty to identify load model parameters is also different, which would lead to the problem of practical identifiability. In this paper, a likelihood profile based parameter practical identifiability analysis method for load model identification is proposed. The load model structure and parameters used for identification and the method to identify parameters based on ambient signal are introduced first. The definition of identifiability together with the likelihood profile analysis method are then proposed, after which the procedures of load model parameter identifiability are given. Simulation is conducted in WSCC 9 bus system to show the effectiveness of the proposed method. Impact factors of load model parameter identifiability are also analyzed and simulated.

KW - Confidential interval

KW - Likelihood profile

KW - Load model identification

KW - Practical parameter identifiability

UR - https://www.scopus.com/pages/publications/85060779907

U2 - 10.1109/PESGM.2018.8586673

DO - 10.1109/PESGM.2018.8586673

M3 - 会议稿件

AN - SCOPUS:85060779907

T3 - IEEE Power and Energy Society General Meeting

BT - 2018 IEEE Power and Energy Society General Meeting, PESGM 2018

PB - IEEE Computer Society

T2 - 2018 IEEE Power and Energy Society General Meeting, PESGM 2018

Y2 - 5 August 2018 through 10 August 2018

ER -

Identifiability Analysis of Load Model Parameter Identification with Likelihood Profile Method

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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