TY - JOUR
T1 - Adaptive Dynamic State Estimation of Distribution Network Based on Interacting Multiple Model
AU - Kong, Xiangyu
AU - Zhang, Xiaopeng
AU - Zhang, Xuanyong
AU - Wang, Chengshan
AU - Chiang, Hsiao Dong
AU - Li, Peng
N1 - Publisher Copyright:
© 2010-2012 IEEE.
PY - 2022/4/1
Y1 - 2022/4/1
N2 - 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.
AB - 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.
KW - Dynamic state estimation
KW - extended Kalman filter
KW - interacting multiple models
KW - unscented Kalman filter
UR - http://www.scopus.com/inward/record.url?scp=85119101390&partnerID=8YFLogxK
U2 - 10.1109/TSTE.2021.3118030
DO - 10.1109/TSTE.2021.3118030
M3 - Article
AN - SCOPUS:85119101390
SN - 1949-3029
VL - 13
SP - 643
EP - 652
JO - IEEE Transactions on Sustainable Energy
JF - IEEE Transactions on Sustainable Energy
IS - 2
ER -