Long Period Continuous Operation Data Sample Generation Method for Power Grid

Yupeng Huang; Nan Yang; Yi Han; Xiaolin Qi

doi:10.1109/ITAIC58329.2023.10408971

Long Period Continuous Operation Data Sample Generation Method for Power Grid

Yupeng Huang^*, Nan Yang, Yi Han, Xiaolin Qi

^*Corresponding author for this work

State Grid Corporation of China

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

Abstract

In recent years, artificial intelligence technology has provided new ideas for solving the problems related to power system operation and control, and the new power system has also provided new scenarios for the application of artificial intelligence technology. Artificial intelligence training needs massive data samples of power grid dispatching and operation, and the quality of training samples is an important factor to determine the training effect. At present, the standard examples often do not contain operation data information, and the actual power grid operation data contains less extreme scenarios, which is difficult to be directly used for artificial intelligence training. This paper proposes a method to generate long-term continuous operation data samples for power grid, and constructs data samples that meet the actual power grid operation characteristics to support artificial intelligence training in multiple scenarios.

Original language	English
Title of host publication	IEEE ITAIC 2023 - IEEE 11th Joint International Information Technology and Artificial Intelligence Conference
Editors	Bing Xu, Kefen Mou
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1776-1780
Number of pages	5
ISBN (Electronic)	9798350333664
DOIs	https://doi.org/10.1109/ITAIC58329.2023.10408971
Publication status	Published - 2023
Externally published	Yes
Event	11th Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2023 - Chongqing, China Duration: 8 Dec 2023 → 10 Dec 2023

Publication series

Name	IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC)
ISSN (Print)	2693-2865

Conference

Conference	11th Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2023
Country/Territory	China
City	Chongqing
Period	8/12/23 → 10/12/23

Keywords

data sample generation
long period continuous operation
simulation model

Access to Document

10.1109/ITAIC58329.2023.10408971

Cite this

Huang, Y., Yang, N., Han, Y., & Qi, X. (2023). Long Period Continuous Operation Data Sample Generation Method for Power Grid. In B. Xu, & K. Mou (Eds.), IEEE ITAIC 2023 - IEEE 11th Joint International Information Technology and Artificial Intelligence Conference (pp. 1776-1780). (IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC)). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITAIC58329.2023.10408971

Huang, Yupeng ; Yang, Nan ; Han, Yi et al. / Long Period Continuous Operation Data Sample Generation Method for Power Grid. IEEE ITAIC 2023 - IEEE 11th Joint International Information Technology and Artificial Intelligence Conference. editor / Bing Xu ; Kefen Mou. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 1776-1780 (IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC)).

@inproceedings{ea53953782a74633900954cf6425b550,

title = "Long Period Continuous Operation Data Sample Generation Method for Power Grid",

abstract = "In recent years, artificial intelligence technology has provided new ideas for solving the problems related to power system operation and control, and the new power system has also provided new scenarios for the application of artificial intelligence technology. Artificial intelligence training needs massive data samples of power grid dispatching and operation, and the quality of training samples is an important factor to determine the training effect. At present, the standard examples often do not contain operation data information, and the actual power grid operation data contains less extreme scenarios, which is difficult to be directly used for artificial intelligence training. This paper proposes a method to generate long-term continuous operation data samples for power grid, and constructs data samples that meet the actual power grid operation characteristics to support artificial intelligence training in multiple scenarios.",

keywords = "data sample generation, long period continuous operation, simulation model",

author = "Yupeng Huang and Nan Yang and Yi Han and Xiaolin Qi",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 11th Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2023 ; Conference date: 08-12-2023 Through 10-12-2023",

year = "2023",

doi = "10.1109/ITAIC58329.2023.10408971",

language = "English",

series = "IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1776--1780",

editor = "Bing Xu and Kefen Mou",

booktitle = "IEEE ITAIC 2023 - IEEE 11th Joint International Information Technology and Artificial Intelligence Conference",

address = "United States",

}

Huang, Y, Yang, N, Han, Y & Qi, X 2023, Long Period Continuous Operation Data Sample Generation Method for Power Grid. in B Xu & K Mou (eds), IEEE ITAIC 2023 - IEEE 11th Joint International Information Technology and Artificial Intelligence Conference. IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC), Institute of Electrical and Electronics Engineers Inc., pp. 1776-1780, 11th Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2023, Chongqing, China, 8/12/23. https://doi.org/10.1109/ITAIC58329.2023.10408971

Long Period Continuous Operation Data Sample Generation Method for Power Grid. / Huang, Yupeng; Yang, Nan; Han, Yi et al.
IEEE ITAIC 2023 - IEEE 11th Joint International Information Technology and Artificial Intelligence Conference. ed. / Bing Xu; Kefen Mou. Institute of Electrical and Electronics Engineers Inc., 2023. p. 1776-1780 (IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC)).

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

TY - GEN

T1 - Long Period Continuous Operation Data Sample Generation Method for Power Grid

AU - Huang, Yupeng

AU - Yang, Nan

AU - Han, Yi

AU - Qi, Xiaolin

PY - 2023

Y1 - 2023

N2 - In recent years, artificial intelligence technology has provided new ideas for solving the problems related to power system operation and control, and the new power system has also provided new scenarios for the application of artificial intelligence technology. Artificial intelligence training needs massive data samples of power grid dispatching and operation, and the quality of training samples is an important factor to determine the training effect. At present, the standard examples often do not contain operation data information, and the actual power grid operation data contains less extreme scenarios, which is difficult to be directly used for artificial intelligence training. This paper proposes a method to generate long-term continuous operation data samples for power grid, and constructs data samples that meet the actual power grid operation characteristics to support artificial intelligence training in multiple scenarios.

AB - In recent years, artificial intelligence technology has provided new ideas for solving the problems related to power system operation and control, and the new power system has also provided new scenarios for the application of artificial intelligence technology. Artificial intelligence training needs massive data samples of power grid dispatching and operation, and the quality of training samples is an important factor to determine the training effect. At present, the standard examples often do not contain operation data information, and the actual power grid operation data contains less extreme scenarios, which is difficult to be directly used for artificial intelligence training. This paper proposes a method to generate long-term continuous operation data samples for power grid, and constructs data samples that meet the actual power grid operation characteristics to support artificial intelligence training in multiple scenarios.

KW - data sample generation

KW - long period continuous operation

KW - simulation model

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U2 - 10.1109/ITAIC58329.2023.10408971

DO - 10.1109/ITAIC58329.2023.10408971

M3 - Conference contribution

AN - SCOPUS:85186102103

T3 - IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC)

SP - 1776

EP - 1780

BT - IEEE ITAIC 2023 - IEEE 11th Joint International Information Technology and Artificial Intelligence Conference

A2 - Xu, Bing

A2 - Mou, Kefen

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 11th Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2023

Y2 - 8 December 2023 through 10 December 2023

ER -

Huang Y, Yang N, Han Y, Qi X. Long Period Continuous Operation Data Sample Generation Method for Power Grid. In Xu B, Mou K, editors, IEEE ITAIC 2023 - IEEE 11th Joint International Information Technology and Artificial Intelligence Conference. Institute of Electrical and Electronics Engineers Inc. 2023. p. 1776-1780. (IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC)). doi: 10.1109/ITAIC58329.2023.10408971

Long Period Continuous Operation Data Sample Generation Method for Power Grid

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