The case when the source of information provides precise belief function/mass, within the generalized power space, has been studied by many people. However, in many decision situations, the precise belief structure is...The case when the source of information provides precise belief function/mass, within the generalized power space, has been studied by many people. However, in many decision situations, the precise belief structure is not always available. In this case, an interval-valued belief degree rather than a precise one may be provided. So, the probabilistic transformation of imprecise belief function/mass in the generalized power space including Dezert-Smarandache （DSm） model from scalar transformation to sub-unitary interval transformation and, more generally, to any set of sub-unitary interval transformation is provided. Different from the existing probabilistic transformation algorithms that redistribute an ignorance mass to the singletons involved in that ignorance pro- portionally with respect to the precise belief function or probability function of singleton, the new algorithm provides an optimization idea to transform any type of imprecise belief assignment which may be represented by the union of several sub-unitary （half-） open intervals, （half-） closed intervals and/or sets of points belonging to [0,1]. Numerical examples are provided to illustrate the detailed implementation process of the new probabilistic transformation approach as well as its validity and wide applicability.展开更多
随着大量新能源场站接入交直流混联电网,系统的静态电压稳定裕度(static voltage stability margin,SVSM)水平具有很大的不确定性,需要研究考虑新能源场站高阶不确定性的交直流混联电网SVSM计算方法。针对此问题,首先建立了交直流混联电...随着大量新能源场站接入交直流混联电网,系统的静态电压稳定裕度(static voltage stability margin,SVSM)水平具有很大的不确定性,需要研究考虑新能源场站高阶不确定性的交直流混联电网SVSM计算方法。针对此问题,首先建立了交直流混联电网SVSM计算模型,模型中考虑了直流换流站控制方式随负荷增长的切换;采用概率盒模型描述风速与光照强度的随机波动,提出了改进区间半不变量法以获得更准确的SVSM概率盒,该方法通过K-means++聚类算法将随机变量样本划分为多个波动范围较小的样本集,以降低半不变量的线性化计算带来的误差;并结合Gram-Charlier级数展开和概率加权和计算得到考虑新能源场站高阶不确定性的系统SVSM概率盒。通过对修改的IEEE-39节点交直流系统和南方电网两个算例的分析,并与区间半不变量法和双层蒙特卡洛法比较,验证了所提出方法获得的SVSM概率盒具有较高的计算精度和效率。展开更多
基金supported by the National Natural Science Foundation of China (60572161 60874105)+5 种基金the Excellent Ph.D. Paper Author Foundation of China (200443)the Postdoctoral Science Foundation of China (20070421094)the Program for New Century Excellent Talents in University (NCET-08-0345)the Shanghai Rising-Star Program(09QA1402900)the "Chenxing" Scholarship Youth Found of Shanghai Jiaotong University (T241460612)the Ministry of Education Key Laboratory of Intelligent Computing & Signal Processing (2009ICIP03)
文摘The case when the source of information provides precise belief function/mass, within the generalized power space, has been studied by many people. However, in many decision situations, the precise belief structure is not always available. In this case, an interval-valued belief degree rather than a precise one may be provided. So, the probabilistic transformation of imprecise belief function/mass in the generalized power space including Dezert-Smarandache （DSm） model from scalar transformation to sub-unitary interval transformation and, more generally, to any set of sub-unitary interval transformation is provided. Different from the existing probabilistic transformation algorithms that redistribute an ignorance mass to the singletons involved in that ignorance pro- portionally with respect to the precise belief function or probability function of singleton, the new algorithm provides an optimization idea to transform any type of imprecise belief assignment which may be represented by the union of several sub-unitary （half-） open intervals, （half-） closed intervals and/or sets of points belonging to [0,1]. Numerical examples are provided to illustrate the detailed implementation process of the new probabilistic transformation approach as well as its validity and wide applicability.
文摘随着大量新能源场站接入交直流混联电网,系统的静态电压稳定裕度(static voltage stability margin,SVSM)水平具有很大的不确定性,需要研究考虑新能源场站高阶不确定性的交直流混联电网SVSM计算方法。针对此问题,首先建立了交直流混联电网SVSM计算模型,模型中考虑了直流换流站控制方式随负荷增长的切换;采用概率盒模型描述风速与光照强度的随机波动,提出了改进区间半不变量法以获得更准确的SVSM概率盒,该方法通过K-means++聚类算法将随机变量样本划分为多个波动范围较小的样本集,以降低半不变量的线性化计算带来的误差;并结合Gram-Charlier级数展开和概率加权和计算得到考虑新能源场站高阶不确定性的系统SVSM概率盒。通过对修改的IEEE-39节点交直流系统和南方电网两个算例的分析,并与区间半不变量法和双层蒙特卡洛法比较,验证了所提出方法获得的SVSM概率盒具有较高的计算精度和效率。
