Distribution Network Fault Segment Location Algorithm Based on Bayesian Estimation in Intelligent Distributed Control Mode

被引：0
|
作者：
Zheng C. [1 ]
Zhu G. [1 ]
机构：
[1] College of Electric Power, South China University of Technology, Guangzhou, 510640, Guangdong Province
来源：
Dianwang Jishu/Power System Technology | 2020年 / 44卷 / 04期
关键词：
Bayesian probability model; Causal relationship; Distributed control mode;
D O I：
10.13335/j.1000-3673.pst.2019.0734
中图分类号：
学科分类号：
摘要：
As distributed protection and control system is more in line with the structural characteristics and operational requirements of smart distribution grid, it becomes an important development direction for future distribution network. Taking intelligent distributed control mode as an example, this paper uses the 'voting' strategy in Byzantine agreement to establish a causal relationship between feeder state and feeder terminal unit (FTU) status in algebraic form, and identify possible fault segments with decision information. Finally, Bayesian probability model is used to identify the fault location best explaining overcurrent information. The proposed method eliminates the problem that centralized fault location system is limited by information transmission distance and the information processing capability of the primary station. For the complex distribution network with T-type coupling node, the proposed method can identify fault accurately in condition of single or multiple faults. Its high-tolerance performance is verified with simulation results. © 2020, Power System Technology Press. All right reserved.
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收藏
页码：1561 / 1567
页数：6
相关论文
共 22 条
  • [11] Liu J., Dong X., Chen X., Et al., Robust fault isolation and restoration for distribution systems, Power System Technology, 36, 1, pp. 253-257, (2012)
  • [12] Guo Z., Xu Q., Hong J., Et al., Integer linear programming based fault section diagnosis method with high fault-tolerance and fast performance for distribution network, Proceedings of the CSEE, 37, 3, pp. 786-794, (2017)
  • [13] Cong W., Lu Q., Tian C., Et al., Smart distribution terminal unit and its standardized modeling, Automation of Electric Power Systems, 37, 10, pp. 6-12, (2013)
  • [14] Liu J., Zhao Q., Cheng H., Et al., Robust fault diagnosis and restoration for distribution grids, Automation of Electric Power Systems, 34, 7, pp. 50-56, (2010)
  • [15] Yu J., Wang K., Qin Y., Et al., Fault location method for intelligent distributed feeder automation of active distribution network, Southern Power System Technology, 10, 12, pp. 38-44, (2016)
  • [16] Du D., Ye Z., Xu Y., A solution of integrated intelligent distributed feeder automation based on GOOSE, Power System Protection and Control, 44, 24, pp. 183-189, (2016)
  • [17] Gao M., Xu B., Fan K., Et al., Distributed feeder automation based on automatic recognition of real-time feeder topology, Automation of Electric Power Systems, 39, 9, pp. 127-131, (2015)
  • [18] Liu J., Zhang X., Chen X., Et al., Fault location and service restoration for distribution networks based on coordination of centralized intelligence and distributed intelligence, Power System Technology, 37, 9, pp. 2608-2614, (2013)
  • [19] Cong W., Zheng Y., Zhang Z., Et al., Distributed storage and management method for topology information of smart distribution network, Automation of Electric Power Systems, 41, 13, pp. 111-118, (2017)
  • [20] Wang J., Zhao Y., Wu J., Byzantine fault tolerant network structure and algorithm in WMN, Computer Engineering, 37, 20, pp. 83-86, (2011)
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