Multi-dimensional situation prediction of digital twin active power grid based on LSTM algorithm

Xun Wang; Yinghui Tan; Tao Li; Chuang Liu; Guanghao Yang; Qian Wang

doi:10.1117/12.3014395

9 January 2024 Multi-dimensional situation prediction of digital twin active power grid based on LSTM algorithm

Xun Wang, Yinghui Tan, Tao Li, Chuang Liu, Guanghao Yang, Qian Wang

Author Affiliations +

Proceedings Volume 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023); 129690Y (2024) https://doi.org/10.1117/12.3014395
Event: International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023), 2023, Qingdao, China

Abstract

In order to understand the multidimensional situation prediction of digital twin active power grid, a research on multidimensional situation prediction of digital twin active power grid based on LSTM algorithm is proposed. In this paper, firstly, a multi-dimensional situation prediction algorithm of power grid key indicators based on LSTM is established to realize the change prediction of key indicators attributes of digital twin active power grid. Secondly, the data of several key indicators such as load characteristics are collected, and a multi-dimensional system prediction model is established, which can control the state of active power grid; The LSTM prediction algorithm is proposed to fit the characteristics of multi-dimensional data, and the next stage of multi-dimensional data prediction is mapped to the power digital twin, so as to realize the synchronous implementation and intelligent regulation of smart energy system operation planning. Finally, a simulation test model is established, and an example shows that the multi-dimensional situation prediction method of digital twin power grid based on deep learning can better predict and distinguish the power grid situation, and provide decision support for accurate planning of energy system in the future.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xun Wang, Yinghui Tan, Tao Li, Chuang Liu, Guanghao Yang, and Qian Wang "Multi-dimensional situation prediction of digital twin active power grid based on LSTM algorithm", Proc. SPIE 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023), 129690Y (9 January 2024); https://doi.org/10.1117/12.3014395

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE