• CSCD核心库收录期刊
  • 中文核心期刊
  • 中国科技核心期刊

ELECTRIC POWER CONSTRUCTION ›› 2022, Vol. 43 ›› Issue (10): 111-120.doi: 10.12204/j.issn.1000-7229.2022.10.011

• Smart Grid • Previous Articles     Next Articles

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

GONG Xundong1(), FEI Youdie2(), LING Jiakai1(), HU Jinfeng1(), QIN Jun1(), WEI Zhinong2(), ZANG Haixiang2()   

  1. 1. Wuxi Power Supply Company of State Grid Jiangsu Electric Power Co., Ltd., Wuxi 214061, Jiangsu Province, China
    2. College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
  • Received:2022-03-23 Online:2022-10-01 Published:2022-09-29
  • Contact: FEI Youdie E-mail:gxd@js.sgcc.com.cn;2286520005@qq.com;lingjiakaiwuxi@163.com;thorpe_113@qq.com;ben_qinjun@125.com;wzn_nj@263.net;zanghaixiang@hhu.edu.cn
  • Supported by:
    State Grid Jiangsu Electric Power Co., Ltd. Research Program(J2021026);National Natural Science Foundation of China(U1966205)


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: