Probabilistic load flow calculation with quasi-Monte Carlo and multiple linear regression

详细信息    查看全文

• 作者：Xiaoyuan Xu ; ^{xuxiaoyuansjtu@gmail.com} ; Zheng Yan
• 关键词：ANOVA ; analysis of variation ; CD ; Cholesky decomposition ; GA ; genetic algorithm ; IS ; importance sampling ; LDS ; low-discrepancy sequence ; LHS ; Latin hypercube sampling ; LSS ; Latin supercube sampling ; MCS ; Monte Carlo simulation ; MLR ; multiple linear regression ; NORTA ; normal to anything model ; PLF ; probabilistic load flow ; QMC ; quasi-Monte Carlo ; RP ; random permutation ; SRS ; simple random sampling
• 刊名：International Journal of Electrical Power & Energy Systems
• 出版年：2017
• 出版时间：June 2017
• 年：2017
• 卷：88
• 期：Complete
• 页码：1-12
• 全文大小：2421 K
• 卷排序：88

文摘

PLF is treated as a low-dimensional problem by the concept of effective dimension. We use low-discrepancy sequences to improve the efficiency of PLF calculation. We propose a multiple linear regression-based technique to control correlations. The MLR-based technique is robust in handling correlated non-normal variables. The proposed PLF method is more efficient compared with other MCS methods.