Monitoring node number selection and assessment method of voltage sag system index

被引:0
|
作者
Xiao X. [1 ]
Tan Y. [1 ]
Hu W. [1 ]
Wang Y. [1 ]
机构
[1] College of Electrical Engineering, Sichuan University, Chengdu
来源
Dianli Zidonghua Shebei/Electric Power Automation Equipment | 2020年 / 40卷 / 10期
基金
中国国家自然科学基金;
关键词
Assessment method; Density biased sampling; Number of monitoring nodes; System index; Voltage sag;
D O I
10.16081/j.epae.202009024
中图分类号
学科分类号
摘要
Accurate evaluation of voltage sag level is the premise of understanding and improving voltage sag. Although IEEE Std 1564-2014 has provided recommendations for the calculation of voltage sag system index,considering that the actual power grid cannot install monitoring devices at all nodes,how to determine the monitoring node number and propose an assessment method suitable for non-uniform data is an unsolved question. Therefore,the determination method of monitoring node number and sampling method is studied. Considering the uneven distribution problem of actual power grid monitoring data,an improved density biased sampling method is proposed. Based on the error margin index,the analytical formula for the number of monitoring nodes is established to meet the requirements of different given errors. Based on the sample data of monitoring nodes,the mean value method is used to calculate the estimated value of system index. Simulative results of IEEE 188-bus system show that the proposed method can not only retain the voltage sag information of original monitoring data in the sampling process,but also can obtain the estima-ted value of monitoring node number required for assessment based on the given errors. Compared with the existing assessment methods,the proposed method has smaller assessment error. © 2020, Electric Power Automation Equipment Press. All right reserved.
引用
收藏
页码:8 / 14
页数:6
相关论文
共 19 条
  • [1] LU Wenqing, CHANG Qiankun, JIA Dongqiang, Et al., Research on evaluation methods of voltage sag severity for equipment side, Electric Power Automation Equipment, 39, 1, pp. 175-182, (2019)
  • [2] IEEE guide for voltage sag indi-ces:IEEE Std 1564-2014, (2014)
  • [3] Guidelines of good practice on the implementation and use of voltage quality monitoring systems for regulatory purposes, (2012)
  • [4] MILANOVIC J V, MEYER J, BALL R F, Et al., International industry practice on power-quality monitoring[J], IEEE Tran-sactions on Power Delivery, 29, 2, pp. 934-941, (2014)
  • [5] QIU Yutao, XIAO Xianyong, XIONG Qian, Et al., Optimal sag monitor placement based on critical impedance and breadth first-condition search, Electric Power Automation Equipment, 34, 10, pp. 138-143, (2014)
  • [6] LIN Fang, XIAO Xianyong, ZHANG Yi, Et al., Optimal monitor allocation and system sag level assessment based on sag information, Electric Power Automation Equipment, 36, 5, pp. 67-73, (2016)
  • [7] MITRA R, GOSWAMI A K, TIWARI P K., Optimal selection of voltage sag mitigating devices for micro-level customer in distribution system[J], IET Renewable Power Generation, 13, 1, pp. 191-200, (2019)
  • [8] ELPHICK S, GOSBELL V, SMITH V, Et al., Assessing network compliance for power quality performance, 2014 16th International Conference on Harmonics and Quality of Power(ICHQP), pp. 317-321, (2014)
  • [9] RONNBERG S K, BOLLEN M., Estimating system indices for short interruptions from data obtained by a limited number of monitors[J], Electric Power Systems Research, 146, pp. 63-70, (2017)
  • [10] pp. 87-88, (2010)