Fault location algorithm based on state estimation in tree distribution network with distributed generation
DOI：10.7667/PSPC171749
Key Words:distributed generation  distribution network  fault location  optimal estimation of discrete model  time-varying gain matrix
Author NameAffiliationE-mail
WANG Yansong College of Information and Control Engineering, China University of Petroleum East China, Qingdao 266580, China  
LIU Shan College of Information and Control Engineering, China University of Petroleum East China, Qingdao 266580, China  
YI Jingbo Shengli Petroleum Administration, Shengli Power Plant, Dongying 257087, China dcyjb@sina.com 
XUE Yongduan College of Information and Control Engineering, China University of Petroleum East China, Qingdao 266580, China  
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Abstract:Fault location of distribution network with multi-generation is of great significance to exclude the fault and improve the power supply reliability. The research in this paper is based on interval location of tree distribution network with distributed generation. The distribution network is divided into two power sections and single power sections according to the interval characteristics, the fault equivalent circuit with its corresponding differential equation and optimal estimation of discrete model are established. The method takes voltage and current of the power supply port and FTU current as output quantity, and the fault distance as state quantity. A strongly tracking Calman filter algorithm is used to solve the optimal estimation of discrete model and track the fault location of interphase short circuit. By building a tree distribution network with DG, the effect of load and transition resistance on fault location is analyzed. The simulation results show that the tracking speed of algorithm is fast and less affected by the initial value. It is not affected by the location or quantity of DG and transition resistance. It is also less affected by the load and tree topology. The location accuracy can meet the engineering accuracy requirements. This work is supported by Natural Science Foundation of Shandong Province (No. ZR2012EEL20), National Natural Science Foundation of China (No. 51477184) and National Key Research and Development Program of China (No. 2018YFB0904800).
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