Integration of Quasi-quantitative and Quantitative Methods in Transient Stability Analysis

被引：0
|
作者：
Lyu R. [1 ,2 ,3 ]
Xue Y. [2 ,3 ]
Huang T. [2 ,3 ]
机构：
[1] School of Automation, Nanjing University of Science and Technology, Nanjing
[2] NARI Group Corporation (State Grid Electric Power Research Institute), Nanjing
[3] State Key Laboratory of Smart Grid Protection and Control, Nanjing
来源：
Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2021年 / 45卷 / 04期
关键词：
Case filtering; Extended equal-area criterion (EEAC); Machine learning; Multiple classification; Online stability analysis; Quasi-quantitative analysis; Transient stability;
D O I：
10.7500/AEPS20210102005
中图分类号：
学科分类号：
摘要：
Extended equal-area criterion (EEAC), a quantitative method of power system transient stability, has the same mode adaptability and analysis accuracy as the step-by-step numerical integration method, and has been widely used in the online analysis of power systems. In order to cope with the new challenges of the increase in system scale and model complexity to the amount of calculation, a case filtering process that combines causal driving and data driving is introduced. A framework is proposed by combining qualitative multiple classification, quasi-quantitative analysis and precise quantitative algorithms, and a "two-layer screening" multi-output classifier is designed. In addition to keep the two time-varying indices in the feature vector of case filtering, a causal index reflecting the variability of the unstable mode has also been added, which improves the quality of the classification result and significantly improves the efficiency and robustness of the case filtering. The overall performance of the case filterer after being integrated into the EEAC engineering software is verified through the simulation test of the 9 provincial power systems in China. © 2021 Automation of Electric Power Systems Press.
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页码：12 / 19
页数：7
相关论文
共 15 条
  • [1] KUNDUR P, BALU N J, LAUBY M G., Power system stability and control, (1994)
  • [2] NI Yixin, CHEN Shousun, ZHANG Baolin, Theory and analysis of dynamics power system, (2002)
  • [3] XUE Yusheng, Quantitative study of general motion stability and an example on power system stability, (1999)
  • [4] XUE Yusheng, EEAC and FASTEST, Automation of Electric Power Systems, 22, 9, pp. 25-30, (1998)
  • [5] XUE Feng, DING Chun, XUE Yusheng, Early termination algorithm for transient stability analysis, Automation of Electric Power Systems, 25, 20, pp. 9-13, (2001)
  • [6] XUE Yusheng, HUANG Tiangang, CHEN Guoping, Et al., Review on case filtering in transient stability analysis, Automation of Electric Power Systems, 43, 6, pp. 1-14, (2019)
  • [7] XUE Yusheng, LAI Yening, Integration of macro energy thinking and big data thinking: Part one big data and power big data, Automation of Electric Power Systems, 40, 1, pp. 1-8, (2016)
  • [8] XUE Yusheng, LAI Yening, Integration of macro energy thinking and big data thinking: Part two applications and explorations, Automation of Electric Power Systems, 40, 8, pp. 1-13, (2016)
  • [9] ZHANG Rui, XU Yan, DONG Zhaoyang, Et al., Post-disturbance transient stability assessment of power systems by a self-adaptive intelligent system, IET Generation, Transmission & Distribution, 9, 3, pp. 296-305, (2015)
  • [10] SUN Hongbin, WANG Kang, ZHANG Boming, Et al., Rule extraction in transient stability study using linear decision trees, Proceedings of the CSEE, 31, 34, pp. 61-67, (2011)
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