Distribution network state estimation based on attention-enhanced recurrent neural network pseudo-measurement modeling

Yaojian Wang; Jie Gu; Lyuzerui Yuan

doi:10.1186/s41601-023-00306-w

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Distribution network state estimation based on attention-enhanced recurrent neural network pseudo-measurement modeling

Original Research | 更新时间：2026-09-30

- Distribution network state estimation based on attention-enhanced recurrent neural network pseudo-measurement modeling
- Distribution network state estimation based on attention-enhanced recurrent neural network pseudo-measurement modeling
- 现代电力系统保护与控制（英文） 2023年8卷第2期页码：1-16
- 作者机构：
  
  Department of Electrical Engineering, School of Electronics, Information and Electrical Engineering, Shanghai Jiao Tong University,Shanghai,China
- 作者简介：
- 基金信息：
- DOI：10.1186/s41601-023-00306-w
  中图分类号：
- 纸质出版：2023-04-01
- 稿件说明：
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Yaojian Wang, Jie Gu, Lyuzerui Yuan. Distribution network state estimation based on attention-enhanced recurrent neural network pseudo-measurement modeling[J]. 现代电力系统保护与控制（英文）, 2023,8(2):1-16.

Yaojian Wang, Jie Gu, Lyuzerui Yuan. Distribution network state estimation based on attention-enhanced recurrent neural network pseudo-measurement modeling[J]. Protection and Control of Modern Power Systems, 2023, 8(2): 1-16.
Yaojian Wang, Jie Gu, Lyuzerui Yuan. Distribution network state estimation based on attention-enhanced recurrent neural network pseudo-measurement modeling[J]. 现代电力系统保护与控制（英文）, 2023,8(2):1-16. DOI： 10.1186/s41601-023-00306-w.

Yaojian Wang, Jie Gu, Lyuzerui Yuan. Distribution network state estimation based on attention-enhanced recurrent neural network pseudo-measurement modeling[J]. Protection and Control of Modern Power Systems, 2023, 8(2): 1-16. DOI： 10.1186/s41601-023-00306-w.

摘要

Because there is insufficient measurement data when implementing state estimation in distribution networks

this paper proposes an attention-enhanced recurrent neural network (A-RNN)-based pseudo-measurement modeling metho. First

based on analyzing the power series at the source and load end in the time and frequency domains

a period-dependent extrapolation model is established to characterize the power series in those domains. The complex mapping functions in the model are automatically represented by A-RNNs to obtain an A-RNNs-based period-dependent pseudo-measurement generation model. The distributed dynamic state estimation model of the distribution network is established

and the pseudo-measurement data generated by the model in real time is used as the input of the state estimation model together with the measurement data. The experimental results show that the method proposed can explore in depth the complex sequence characteristics of the measurement data such that the accuracy of the pseudo-measurement data is further improved. The results also show that the state estimation accuracy of a distribution network is very poor when there is a lack of measurement data

but is greatly improved by adding the pseudo-measurement data generated by the model proposed.

Abstract

Because there is insufcient measurement data when implementing state estimation in distribution networks

this paper proposes an attention-enhanced recurrent neural network (A-RNN)-based pseudo-measurement modeling metho. First

based on analyzing the power series at the source and load end in the time and frequency domains

but is greatly improved by adding the pseudo-measurement data generated by the model proposed.

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