Alternating-Iteration State Estimation of AC/DC Hybrid Distribution System Based on Pseudo-Measurement Modeling Using Deep Neural Network

doi:10.12204/j.issn.1000-7229.2022.10.011

Abstract

Abstract:

At present, a prominent problem in the state estimation of distribution system is the lack of real-time measurements, which makes it difficult to realize the observability of the whole network. In order to provide accurate basic data for distribution management system (DMS), an alternating-iteration state estimation method based on pseudo measurement modeling using deep neural networks (DNN) is proposed. Firstly, the steady-state model of voltage source converter (VSC) and the real-time measurement model of hybrid distribution system are presented. Then the historical measurements are used to train DNN off-line and establish pseudo-measurement models of power injection on load buses. Finally, alternating-iteration state estimation is carried out for AC and DC areas. Only the estimated states of VSC branches need to be exchanged, thus ensuring the consistency of the boundary states. Test results show that the proposed method can accurately estimate the states of hybrid distribution system under low coverage of real-time measurements.

Key words: hybrid distribution system, alternating-iteration state estimation, deep neural network, pseudo measurement

CLC Number:

TM74

GONG Xundong, FEI Youdie, LING Jiakai, HU Jinfeng, QIN Jun, WEI Zhinong, ZANG Haixiang. Alternating-Iteration State Estimation of AC/DC Hybrid Distribution System Based on Pseudo-Measurement Modeling Using Deep Neural Network[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(10): 111-120.

Figures/Tables 16

Fig.1 Equivalent model of VSC

Fig.2 Training of DNN-based pseudo-measurement model

Fig.3 Sub-area of hybrid distribution network

Fig.4 Flow chart of the proposed method

Table 1 Error of the test set

Fig.5 Comparison of DNN outputs

Fig.6 Maximum estimation error of state variables obtained by CSE and AISE with different pseudo measurement sets

Fig.7 Convergence of the AISE dnn

Table 2 Mean estimation error of VSC branch variables

Table 3 Statistical results of calculating time

Fig.A1 Test system 1 of hybrid distribution network

Fig.A2 Test system 2 of hybrid distribution network

Table A1 Model parameters and control modes of VSC in system 1

Table A2 Real measurement configuration of system 1

Table A3 Model parameters and control modes of VSC in system 2

Table A4 Real measurement configuration of system 2

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