电力系统静态电压稳定裕度评估方法

基于大数据集中隐含关系探索方法和电力系统大量的不同运行状态下的潮流数据,探寻出系统中与静态电压稳定相对裕度有最强的关系的变量,并基于这些变量构建相对裕度评估方法.该方法选取一定数量的关系性较强的变量作为相对稳定裕度的最优输入变量,所使用的探索统计工具是最大互信息系数MIC （Maximal Information Coefficient）和皮尔逊相关系数PCC （Pearson Correlation Coefficient）.当所选的最优输入变量的值能从广域量测系统中获得,电力系统的静态电压稳定相对裕度便能够通过所探索出的关系实时地评估出来.结合PSS/E中39节点算例的仿真测试结果,验证了所提方法的有效性.

Assessment method of power system static voltage stability margin

based on explore methods of implication relation in large data sets and power flow data of power system under different running state,the variables that have close relationship to power system static voltage stability margin is explored.And based on what the relative assessment method is created.In this method variables that closely related are selected as the optimal input variables,the statistical tools used in this paper is MIC （maximal information coefficient） and PPC （Pearson correlation coefficient）.When the optimal input variables could be obtained from WAMS （Wide Area Measurement Systems）,the power system static voltage stability margin is assessed through the relationship we explored in real time.The IEEE39-bus system verifies the veracity and validity of this proposed method.