State Estimation Method for Distribution Network Based on Incomplete Measurement Data

doi:10.11930/j.issn.1004-9649.202407002

Abstract

Abstract:

With the large-scale integration of distributed energy resources, the operational characteristics of the traditional distribution networks have undergone significant changes, leading to such problems as dispersed loads, poor real-time observability, and incomplete data, which severely impact the state monitoring and operational optimization of the distribution networks. To address above problems, we propose a distribution network state estimation method based on Bayesian-optimized convolutional neural networks (CNN) and long short-term memory (LSTM) networks with incomplete real-time measurement data. The method is divided into two phases: offline learning and online state estimation. In the offline learning phase, generative adversarial networks are used to generate the required samples for training the CNN-LSTM model, and the Bayesian optimization algorithm is employed to adjust the hyperparameters, thereby enhancing the accuracy of the algorithm. In the online state estimation phase, the state estimation is performed online with incomplete real-time data of the distribution network and the trained CNN-LSTM model. Finally, simulation analysis is conducted on the IEEE 33 and IEEE 123 networks, which confirms the effectiveness and accuracy of the proposed state estimation method.

Key words: distribution network, state estimation, incomplete measurement, convolutional neural networks, long short-term memory, Bayesian optimization

LI Peng, ZU Wenjing, LIU Yixin, TIAN Chunzheng, HAO Yuanzhao, LI Huixuan. State Estimation Method for Distribution Network Based on Incomplete Measurement Data[J]. Electric Power, 2025, 58(5): 1-10.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202407002

https://www.electricpower.com.cn/EN/Y2025/V58/I5/1

Figures/Tables 15

Fig.1 Overall modeling approach

Fig.2 Convolutional neural networks structure

Fig.3 CNN-LSTM combined model structure

Fig.4 CNN-LSTM model optimization process based on Bayesian optimization

Fig.5 Measurement configuration of the IEEE 33-node distribution network

Table 1 Adam parameter settings

参数		数值
β₁		0.9
β₂		0.999
η		e^–8

Fig.6 State estimation results of the voltage magnitude at each node

Fig.7 State estimation results of the voltage phase angle at each node

Fig.8 Absolute error of the voltage magnitude and phase angle at node 2 across different sections

Fig.9 Comparative analysis of the MAE for voltage magnitude across four prediction models

Fig.10 Comparative analysis of the MAE for voltage phase angle across four prediction models

Table 2 Comparative analysis of the MAE for voltage magnitude and phase angle at node 5

算法	电压幅值MAE (10^–3 p.u.)	电压相角MAE (10^–3 p.u.)	状态估计时间/s
WLS	1.28	2.25	0.46
本文方法	0.59	1.16	0.41

Fig.11 Measurement configuration of the IEEE 123-node distribution network

Fig.12 The MAE of the voltage magnitude and phase angle at each node

Table 3 The MAE of the voltage magnitude and phase angle at node 9 with different measurement noise

测量噪声/%	电压幅值MAE(10^–3 p.u.)	电压相角MAE(10^–3 p.u.)
1	1.05	1.35
2	1.28	1.67
3	1.42	2.26

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