Adaptive Dynamic State Estimation of Distribution Network Based on Interacting Multiple Model

Xiangyu Kong, Xiaopeng Zhang, Xuanyong Zhang*, Chengshan Wang, Hsiao Dong Chiang, Peng Li

*Corresponding author for this work

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Abstract

With the large-scale access of all kinds of distributed generations (DGs), the operation mode of the distribution network is increasingly diverse and changeable. To monitor the operation of an active distribution network, an adaptive dynamic estimation method is proposed to address the new generation of power system. Considering the features of different types of operation scenario change of distribution network and DGs, the proposed method uses the state deviation index to identify the current operation mode before state estimation. In the adaptive estimation stage, two typical estimators are improved to cope with the typical operation mode and embedded in the interactive multiple model (IMM) algorithm framework. IMM uses the identification results of operation mode to give higher weight to the corresponding estimator and finally outputs the joint estimation results. The proposed estimation method is investigated in an improved IEEE 33-bus system and an actual distribution network in China, which results indicate the proposed method converges more quickly and maintains better accuracy while facing the complex distribution network.

Original languageEnglish
Pages (from-to)643-652
Number of pages10
JournalIEEE Transactions on Sustainable Energy
Volume13
Issue number2
DOIs
Publication statusPublished - 1 Apr 2022
Externally publishedYes

Keywords

  • Dynamic state estimation
  • extended Kalman filter
  • interacting multiple models
  • unscented Kalman filter

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Kong, X., Zhang, X., Zhang, X., Wang, C., Chiang, H. D., & Li, P. (2022). Adaptive Dynamic State Estimation of Distribution Network Based on Interacting Multiple Model. IEEE Transactions on Sustainable Energy, 13(2), 643-652. https://doi.org/10.1109/TSTE.2021.3118030