文章摘要
黄天罡,薛禹胜,陈国平,等.暂态稳定算例的高效筛除[J].电力系统自动化,2018,42(8):83-91. DOI: 10.7500/AEPS20180302011.
HUANG Tiangang,XUE Yusheng,CHEN Guoping, et al.An Efficient Stable Case Screening Algorithm for Transient Stability Assessment[J].Automation of Electric Power Systems,2018,42(8):83-91. DOI: 10.7500/AEPS20180302011.
暂态稳定算例的高效筛除
An Efficient Stable Case Screening Algorithm for Transient Stability Assessment
DOI:10.7500/AEPS20180302011
关键词: 暂态稳定  定量分析  扩展等面积准则  时变性  稳定算例的筛除  研究范式的融合  强壮性
KeyWords: transient stability  quantitative analysis  extended equal area criterion(EEAC)  time-varying property  stable case screening  integration of research paradigms  robustness
上网日期:2018-03-16
基金项目:国家重点研发计划资助项目(2017YFB0903000);国家自然科学基金重点项目(61533010);国家电网公司科技项目“基于轨迹特征根的电力系统振荡基础理论、算法开发及应用验证”
作者单位E-mail
黄天罡 东南大学电气工程学院, 江苏省南京市 210096
南瑞集团(国网电力科学研究院)有限公司, 江苏省南京市 211106 
huangtiangang@sgepri.sgcc.com.cn 
薛禹胜 南瑞集团(国网电力科学研究院)有限公司, 江苏省南京市 211106
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 
 
陈国平 国家电力调度控制中心, 北京市100031  
薛峰 南瑞集团(国网电力科学研究院)有限公司, 江苏省南京市 211106
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 
 
文福拴 浙江大学电气工程学院, 浙江省杭州市 310027  
摘要:
      高效筛除稳定算例,是在不会将失稳故障误判为稳定的前提下,降低对算例集分析所需的总计算量。将扩展等面积准则(EEAC)的积分步长及映射步长同样减小,可以提高量化精度,但当步长减小到一定程度以后,精度将不再明显提升。将完全忽略时变因素的静态EEAC的结果与部分计入时变因素的动态EEAC的结果相比较,若两者给出的稳定裕度值足够大,且足够接近,就可提前终止EEAC的完整流程,直接确认该算例稳定。将此因果关系引入机器学习系统,获取算例稳定的充分性(非必要性)判据,并优化其相关阈值,通过分层框架筛除风险的稳定算例。在判据与阈值都不加调整的情况下,用中国7个实际电力系统的28套数据考核其强壮性。包括支路两端不同时开断的故障在内,未发生上述风险性误判,而总计算量只有完整EEAC流程的27.9%。
Abstract:
      Efficient stable case screening algorithm can reduce the computational burden of transient stability assessment if the actual unstable fault is not recognized as stable. The quantization accuracy can be improved while both the integral and mapping steps decrease in extended equal area criterion(EEAC)algorithm. However, when the integral and mapping steps are both decreased to a certain extent, the quantization accuracy may no longer be improved obviously. Comparing the results of static EEAC(SEEAC), which neglects all time-varying factors, and dynamic EEAC(DEEAC), which partially considers the time-varying factors, if both stability margins obtained by these two algorithms are large enough and their difference is small enough, the case can be confirmed as stable, and the complete process of EEAC algorithm can be early terminated. The sufficient but unnecessary rules for filtering stable cases can be acquired when this causal relationship is introduced to the machine learning system, and their related threshold values can be optimized with statistical analysis. Then the stable cases can be hierarchically screened out. Its robust performance with the unified rules and threshold values is verified by 28 sets of data from 7 actual Chinese provincial power systems under various operating conditions, that is, for cases with the two ends of the faulted branch opened at different time, no any actual unstable cases is filtered out and the whole computational burden is reduced by 72. 1% comparing to the complete process of EEAC algorithm.
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