Probabilistic Load Flow Evaluation With Extended Latin Hypercube Sampling

For probabilistic load flow(PLF) analysis,Latin hypercube sampling(LHS) has higher efficiency than simple Monte Carlo simulation(MCS).But sample size should be known in advance and fixed in conventional LHS(CLHS).Due to these disadvantages of existing CLHS,extended LHS(ELHS) was proposed and applied in PLF analysis.Started with a LHS of sample size N and associated rank correlation,the extended procedure constructed a new LHS of sample size 2N.The new LHS contains the elements of the original LHS and has a rank correlation that is close to the original rank correlation.While increasing the sample size,the extended method retained the load flow results already obtained.Since the sample size could not be determined in advance,practical convergence criterion of ELHS was described by coefficient of variation ratio before and after the extension.PLF studies with MCS,CLHS,and ELHS were carried out on IEEE 30-bus and IEEE 118-bus test systems respectively.The proposed method could obtain convergence trend for ELHS at different sample sizes while the accuracy was maintained.The results verified the effectiveness,accuracy and expansibility of the proposed method.