Instantaneous sensitivity identification in power systems - Challenges and technique roadmap

Junbo Zhang; Lin Guan; C. Y. Chung

doi:10.1109/PESGM.2016.7741335

Instantaneous sensitivity identification in power systems - Challenges and technique roadmap

Junbo Zhang, Lin Guan, C. Y. Chung

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

14 Citations (Scopus)

Abstract

Statistical machine learning methods based on operational data are believed to have high potentials to solve the mismatch problems in power system models, vis-à-vis the real time operational conditions. As a fundamental tool in the field of situational awareness, the instantaneous sensitivity identification (ISI) using data driven methods also raises with high expectations, but in reality, it suffers from many practical issues, including data collection, evaluation, storage, and analysis problems. After spending more than five years in this research area, we now proposes a technique roadmap for online ISI with the main purpose of clarifying the challenges, analyzing the existed technologies and proposing a solution path. This paper focuses on the whole picture of the future work rather than a detailed algorithm.

Original language	English
Title of host publication	2016 IEEE Power and Energy Society General Meeting, PESGM 2016
Publisher	IEEE Computer Society
ISBN (Electronic)	9781509041688
DOIs	https://doi.org/10.1109/PESGM.2016.7741335
Publication status	Published - 10 Nov 2016
Externally published	Yes
Event	2016 IEEE Power and Energy Society General Meeting, PESGM 2016 - Boston, United States Duration: 17 Jul 2016 → 21 Jul 2016

Publication series

Name	IEEE Power and Energy Society General Meeting
Volume	2016-November
ISSN (Print)	1944-9925
ISSN (Electronic)	1944-9933

Conference

Conference	2016 IEEE Power and Energy Society General Meeting, PESGM 2016
Country/Territory	United States
City	Boston
Period	17/07/16 → 21/07/16

Keywords

Collinearity
Data explosion
Instantaneous sensitivity identification
Locally weighted linear regression
Statistical machine learning

ASJC Scopus subject areas

Energy Engineering and Power Technology
Nuclear Energy and Engineering
Renewable Energy, Sustainability and the Environment
Electrical and Electronic Engineering

More information

10.1109/PESGM.2016.7741335

Cite this

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title = "Instantaneous sensitivity identification in power systems - Challenges and technique roadmap",

abstract = "Statistical machine learning methods based on operational data are believed to have high potentials to solve the mismatch problems in power system models, vis-{\`a}-vis the real time operational conditions. As a fundamental tool in the field of situational awareness, the instantaneous sensitivity identification (ISI) using data driven methods also raises with high expectations, but in reality, it suffers from many practical issues, including data collection, evaluation, storage, and analysis problems. After spending more than five years in this research area, we now proposes a technique roadmap for online ISI with the main purpose of clarifying the challenges, analyzing the existed technologies and proposing a solution path. This paper focuses on the whole picture of the future work rather than a detailed algorithm.",

keywords = "Collinearity, Data explosion, Instantaneous sensitivity identification, Locally weighted linear regression, Statistical machine learning",

author = "Junbo Zhang and Lin Guan and Chung, {C. Y.}",

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year = "2016",

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doi = "10.1109/PESGM.2016.7741335",

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Zhang, J, Guan, L & Chung, CY 2016, Instantaneous sensitivity identification in power systems - Challenges and technique roadmap. in 2016 IEEE Power and Energy Society General Meeting, PESGM 2016., 7741335, IEEE Power and Energy Society General Meeting, vol. 2016-November, IEEE Computer Society, 2016 IEEE Power and Energy Society General Meeting, PESGM 2016, Boston, United States, 17/07/16. https://doi.org/10.1109/PESGM.2016.7741335

Instantaneous sensitivity identification in power systems - Challenges and technique roadmap. / Zhang, Junbo; Guan, Lin; Chung, C. Y.
2016 IEEE Power and Energy Society General Meeting, PESGM 2016. IEEE Computer Society, 2016. 7741335 (IEEE Power and Energy Society General Meeting; Vol. 2016-November).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

TY - GEN

T1 - Instantaneous sensitivity identification in power systems - Challenges and technique roadmap

AU - Zhang, Junbo

AU - Guan, Lin

AU - Chung, C. Y.

PY - 2016/11/10

Y1 - 2016/11/10

N2 - Statistical machine learning methods based on operational data are believed to have high potentials to solve the mismatch problems in power system models, vis-à-vis the real time operational conditions. As a fundamental tool in the field of situational awareness, the instantaneous sensitivity identification (ISI) using data driven methods also raises with high expectations, but in reality, it suffers from many practical issues, including data collection, evaluation, storage, and analysis problems. After spending more than five years in this research area, we now proposes a technique roadmap for online ISI with the main purpose of clarifying the challenges, analyzing the existed technologies and proposing a solution path. This paper focuses on the whole picture of the future work rather than a detailed algorithm.

AB - Statistical machine learning methods based on operational data are believed to have high potentials to solve the mismatch problems in power system models, vis-à-vis the real time operational conditions. As a fundamental tool in the field of situational awareness, the instantaneous sensitivity identification (ISI) using data driven methods also raises with high expectations, but in reality, it suffers from many practical issues, including data collection, evaluation, storage, and analysis problems. After spending more than five years in this research area, we now proposes a technique roadmap for online ISI with the main purpose of clarifying the challenges, analyzing the existed technologies and proposing a solution path. This paper focuses on the whole picture of the future work rather than a detailed algorithm.

KW - Collinearity

KW - Data explosion

KW - Instantaneous sensitivity identification

KW - Locally weighted linear regression

KW - Statistical machine learning

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M3 - Conference article published in proceeding or book

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T3 - IEEE Power and Energy Society General Meeting

BT - 2016 IEEE Power and Energy Society General Meeting, PESGM 2016

PB - IEEE Computer Society

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ER -

Instantaneous sensitivity identification in power systems - Challenges and technique roadmap

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Keywords

ASJC Scopus subject areas

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