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

Rui LYU; Yusheng XUE; Tiangang HUANG

doi:10.7500/AEPS20210102005

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

Basic Research | 更新时间：2025-06-16

- Integration of Quasi-quantitative and Quantitative Methods in Transient Stability Analysis
- Automation of Electric Power Systems Vol. 45, Issue 4, Pages: 12-19(2021)
- 作者机构：
  
  1.南京理工大学自动化学院，江苏省南京市 210094
  2.南瑞集团有限公司（国网电力科学研究院有限公司），江苏省南京市 211106
  3.智能电网保护和运行控制国家重点实验室，江苏省南京市 211106
- 作者简介：
- 基金信息：
  
  State Grid Corporation of China
- DOI：10.7500/AEPS20210102005
  CLC：
- Received：02 January 2021，
  
  Revised：2021-01-12，
  
  Published Online：22 January 2021，
  
  Published：25 February 2021
- 稿件说明：
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吕睿,薛禹胜,黄天罡.准定量法与精确定量法在暂态稳定分析中的融合[J].电力系统自动化,2021,45(4):12-19.

LYU Rui,XUE Yusheng,HUANG Tiangang.Integration of Quasi-quantitative and Quantitative Methods in Transient Stability Analysis[J].Automation of Electric Power Systems,2021,45(4):12-19.
吕睿,薛禹胜,黄天罡.准定量法与精确定量法在暂态稳定分析中的融合[J].电力系统自动化,2021,45(4):12-19. DOI： 10.7500/AEPS20210102005.

LYU Rui,XUE Yusheng,HUANG Tiangang.Integration of Quasi-quantitative and Quantitative Methods in Transient Stability Analysis[J].Automation of Electric Power Systems,2021,45(4):12-19. DOI： 10.7500/AEPS20210102005.

摘要

电力系统暂态稳定性的量化方法——扩展等面积准则（EEAC）具有与数值积分法同样的模型适应性与分析精度，已广泛应用于电力系统的在线分析。为了应对系统规模和模型复杂性的增加对计算量的新挑战，引入因果驱动与数据驱动相融合的算例筛选环节。文中提出定性多档分类、准定量分析，以及精确量化算法三者结合的框架，设计了“两层筛选”的多输出分类器。在分类特征向量中除继续采用时变度指标外，还增加了反映失稳模式易变度的因果型指标，提升了算例分类的质量，并显著提高了算例筛选的效率和强壮性。通过对中国9个网省级电力系统的仿真测试，验证了该筛选分类器融入EEAC工程软件后的整体性能。

Abstract

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.

关键词

Keywords

references

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