3/22/2023 0 Comments Latin hypercube sampling![]() H02J3/00- Circuit arrangements for ac mains or ac distribution networks.H02J- CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER SYSTEMS FOR STORING ELECTRIC ENERGY.H02- GENERATION CONVERSION OR DISTRIBUTION OF ELECTRIC POWER.238000006243 chemical reaction Methods 0.000 description 1.238000004458 analytical method Methods 0.000 description 1.238000005516 engineering process Methods 0.000 description 2.238000000342 Monte Carlo simulation Methods 0.000 description 4.239000000284 extract Substances 0.000 claims description 4.238000004422 calculation algorithm Methods 0.000 claims description 4.238000005315 distribution function Methods 0.000 claims abstract description 9.230000001186 cumulative Effects 0.000 claims abstract description 9.239000011159 matrix material Substances 0.000 claims abstract description 65.238000004364 calculation method Methods 0.000 title claims abstract description 22.238000005070 sampling Methods 0.000 title claims abstract description 25.241000039077 Copula Species 0.000 title claims abstract description 25.Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.) Filing date Publication date Application filed by Nari Technology Co Ltd, NARI Nanjing Control System Co Ltd, State Grid Ningxia Electric Power Co Ltd filed Critical Nari Technology Co Ltd Priority to CN201610147698.9A priority Critical patent/CN105790258B/en Publication of CN105790258A publication Critical patent/CN105790258A/en Application granted granted Critical Publication of CN105790258B publication Critical patent/CN105790258B/en Status Active legal-status Critical Current Anticipated expiration legal-status Critical Links Original Assignee Nari Technology Co Ltd NARI Nanjing Control System Co Ltd State Grid Ningxia Electric Power Co Ltd Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.) ( en Inventor 戴则梅 喻洁 刘莉莉 陈仁思 葛俊 贺文 宁波 张慧玲 韩红卫 丁恰 张丙金 Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.) Granted Application number CN201610147698.9A Other languages Chinese ( zh) Google Patents Latin hypercube sampling method probabilistic power flow calculation method based on normal Copula functionĭownload PDF Info Publication number CN105790258A CN105790258A CN201610147698.9A CN201610147698A CN105790258A CN 105790258 A CN105790258 A CN 105790258A CN 201610147698 A CN201610147698 A CN 201610147698A CN 105790258 A CN105790258 A CN 105790258A Authority CN China Prior art keywords variable new energy matrix electricity generation Prior art date Legal status (The legal status is an assumption and is not a legal conclusion. Google Patents CN105790258A - Latin hypercube sampling method probabilistic power flow calculation method based on normal Copula function Scripting on this page enhances content navigation, but does not change the content in any way.CN105790258A - Latin hypercube sampling method probabilistic power flow calculation method based on normal Copula function ![]() Use Latin Hypercube sampling when you are concerned primarily with the accuracy of the simulation statistics. (Compared to most simulation results, this extra overhead is minor.) The added expense of this method is the extra memory required to track which segments have been sampled while the simulation runs. Latin Hypercube sampling requires fewer trials to achieve the same level of statistical accuracy as Monte Carlo sampling. Latin Hypercube sampling is generally more precise when calculating simulation statistics than is conventional Monte Carlo sampling, because the entire range of the distribution is sampled more evenly and consistently. The Sample Size option (displayed when you select Run Preferences, then Sample), controls the number of segments in the sample. After has sampled each segment exactly once, the process repeats until the simulation stops. This collection of values forms the Latin Hypercube sample. While a simulation runs, selects a random assumption value for each segment according to the segment’s probability distribution. ![]()
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