Active distribution network operating situation prediction based on ICEEMDAN‑TA‑LSTM model

Authors

Shu LIU, Electric Power Research Institute，State Grid Shanghai Electric Power Company, Shanghai 200437, China
Shangkun YAO, College of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China
Min ZHOU, Pudong Power Supply Company,State Grid Shanghai Electric Power Company，Shanghai 200120，China
Feng ZHU, College of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China
Shuxin TIAN, College of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, ChinaFollow
Wenyuan XIAO, College of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China

Keywords

active distribution network; operational dynamics; improved manta ray seeking optimization algorithm; triple attention mechanism; fluctuation coefficient

Abstract

The active distribution network operation situation prediction is an important tool to guarantee the safety and stability of the distribution network and the hazard perception. For the fast and accurate prediction of active distribution network operation, this paper proposes an active distribution network short‑term operation prediction method based on ICEEMDAN‑TA‑LSTM model. Firstly, the original sequence is decomposed into several stable time series components by improving the modal decomposition to reduce the irregularity of the original data. At the same time, the improved manta ray feeding optimization algorithm is used to optimize the model's hyperparameters to comprehensively improve the overall prediction accuracy of the model. Then, from the perspective of nodes and branches, the node voltage overrun margin, branch load severity and voltage/current fluctuation evaluation indexes are proposed to characterize the distribution network operation situation at multiple levels. Finally, the feasibility and effectiveness of the model proposed in this paper are verified by taking the improved IEEE 33 node as a typical calculation example.

DOI

10.19781/j.issn.1673-9140.2023.06.019

First Page

175

Last Page

186

Recommended Citation

LIU, Shu; YAO, Shangkun; ZHOU, Min; ZHU, Feng; TIAN, Shuxin; and XIAO, Wenyuan (2024) "Active distribution network operating situation prediction based on ICEEMDAN‑TA‑LSTM model," Journal of Electric Power Science and Technology: Vol. 38: Iss. 6, Article 19.
DOI: 10.19781/j.issn.1673-9140.2023.06.019
Available at: https://jepst.researchcommons.org/journal/vol38/iss6/19