Random evaluation method of voltage sag characteristics based on scenario construction

被引：0
|
作者：
Xu Y. [1 ]
Sun J. [1 ]
Ding K. [2 ]
Li W. [2 ]
Hu P. [2 ]
机构：
[1] School of Electrical Engineering and Automation, Wuhan University, Wuhan
[2] Institute of Electric Power Science, State Grid Hubei Electric Power Co., Ltd., Wuhan
来源：
| 1600年 / Power System Protection and Control Press卷 / 49期
基金：
中国国家自然科学基金;
关键词：
Distribution network; Random prediction; Sensitive load; Short circuit calculation; Voltage sag;
D O I：
10.19783/j.cnki.pspc.200796
中图分类号：
学科分类号：
摘要：
The distribution of voltage sag amplitude and duration is the main characteristic quantity of voltage sag, and it is also an important basis for sensitive load loss estimation and treatment scheme selection. However, the existing stochastic evaluation methods often have shortcomings of needing a lot of calculation and having low accuracy. This makes it difficult to apply the obtained characteristic value of voltage sag in practice. In view of the characteristics of distribution network with many nodes and obvious load variation, the scenario method is applied to the voltage sag characteristic evaluation. First, the K-medoids clustering method is used to classify the node types and select the typical nodes. Secondly, a multidimensional scene dimension reduction of the initial operation state of the distribution network is realized through the node weighted network equivalence, and then the short-circuit fault probability distribution curve and network impedance conversion are combined to propose a generation method of a typical voltage sag scene set. Finally, taking a distribution network structure as an example, the characteristic value of voltage sag at sensitive load points in the power supply area is calculated to verify the effectiveness and feasibility of the method. © 2021 Power System Protection and Control Press.
引用
收藏
页码：105 / 112
页数：7
相关论文
共 27 条
  • [1] SHA K S, JAYA L A., Data mining for classification of power quality problems using WEKA and the effect of attributes on classification accuracy, Protection and Control of Modern Power Systems, 3, 3, pp. 303-314, (2018)
  • [2] LU Ganyun, JIANG Xiaowei, HAO Sipeng, Et al., Voltage sag source location based on semi supervised support vector machine, Power System Protection and Control, 47, 18, pp. 76-81, (2019)
  • [3] LIU Jiahan, CHEN Kexu, MA Jian, Et al., Three phase voltage sag classification based on convolution neural network and random forest, Power System Protection and Control, 47, 20, pp. 112-118, (2019)
  • [4] ZHANG Xinjie, GE Shaoyun, LIU Hong, Et al., Intelligent distribution network comprehensive evaluation system and method, Power System Technology, 38, 1, pp. 40-46, (2014)
  • [5] FEBRIANTO W A, GUNARTONO I, PENANGSANG O, Et al., Fault location and voltage sag analysis in electric distribution network, 2018 International Seminar on Intelligent Technology and Its Applications (ISITIA), (2018)
  • [6] PARK C, JANG G., Systematic method to identify an area of vulnerability to voltage sags, IEEE Transactions on Power Delivery, 32, 3, pp. 1583-1591, (2017)
  • [7] MILANOVIC J V, AUNG M T, GUPTA C P., The influence of fault distribution on stochastic prediction of voltage sags, IEEE Transactions on Power Delivery, 20, 1, pp. 278-285, (2005)
  • [8] WU Nan, SHI Mingming, SUN Jian, Et al., Voltage sag simulation method based on Thevenin equivalent, Electrical Measurement & Instrumentation, 56, 24, pp. 80-85, (2019)
  • [9] ZENG Jiang, QIU Guobin, HUANG Yiying, Rapid distribution network sag assessment based on equivalent algorithm and analytical fitting, Electrical Measurement & Instrumentation, 57, 9, pp. 26-32, (2020)
  • [10] WANG Ying, SHAO Bin, XIAO Xianyong, An evaluation method for interruption probability of voltage sag industrial process considering redundancy, Electrical Measurement & Instrumentation, 57, 7, pp. 8-15, (2020)
123