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Space Transformation-Based Interdependency Modelling for Probabilistic Load Flow Analysis of Power Systems
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作者 李雪 陈豪杰 +1 位作者 路攀 杜大军 《Journal of Donghua University(English Edition)》 EI CAS 2016年第5期734-739,共6页
Dependence among random input variables affects importantly the results of probabilistic load flow(PLF),system economic operation,and system security.To solve this problem,the main objectiveness of the paper is to ana... Dependence among random input variables affects importantly the results of probabilistic load flow(PLF),system economic operation,and system security.To solve this problem,the main objectiveness of the paper is to analyze the performance of several schemes for simulating correlated variables combined with the point estimate method(PEM).Unlike the existing works that considering one single scheme combined with Monte Carlo simulation(MCS) or PEM,by neglecting the correlation among random input variables,four schemes were presented for disposing the dependence of correlated random variables,including Nataf transformation /polynomial normal transformation(PINT) combined with orthogonal transformation(OT) / elementary transformation(ET).Combining with the 2m+1 approach of PEM,a space transformation-based formulation was proposed and adopted for solving the PLF.The proposed approach is applied in the modified IEEE 30-bus system while considering correlated wind generations and load demands.Numerical results show the effectiveness of the proposed approach compared with those obtained from the MCS.Results also show that the scheme of combining Nataf transformation and ET with PEM provides the best performance. 展开更多
关键词 elementary transformation(ET) Nataf transformation orthogonal transformation(OT) point estimate method(PEM) polynomial normal transformation(PNT) probabilistic load flow(PLF) space transformation wind and load correlation
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