Improved Latin hypercube sampling method suitable for non-positive correlation control

A Latin hypercube and correlation technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve the problems that non-positive definite matrices cannot be obtained, the cumulative distribution function of input variables is difficult to obtain accurately or is unknown, and achieve calculation Fast speed, small error, and the effect of expanding the scope of application

Active Publication Date: 2017-12-05
SOUTHEAST UNIV
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Problems solved by technology

[0004] The present invention is aimed at the technical problems existing in the prior art, and provides an improved Latin hypercube sampling method suitable for non-positive definite correlation control, which solves the problem that the cumulative distribution functi...

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  • Improved Latin hypercube sampling method suitable for non-positive correlation control
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  • Improved Latin hypercube sampling method suitable for non-positive correlation control

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Embodiment 1

[0058] The present invention provides an implementation case of the improved Latin hypercube sampling method suitable for non-positive definite correlation control:

[0059] The determination of cumulative distribution function and discrete data matrix in step S1 includes:

[0060] Connect distributed power to 5 nodes of PG&E69 node system. Taking photovoltaics as an example, the photovoltaic model adopts the beta distribution model, the selection of shape parameters is: α=0.9, β=0.85, and the capacity is 100kVA. Determine the cumulative distribution function of the input variable as The correlation coefficient matrix between 5 photovoltaic output power samples is P 5 .

[0061] In step S2, when the distribution function is known, sampling by importance sampling method includes:

[0062] Sampling principle The value of the kth variable a is: when x=x ka hour, Z k (x ka )have the maximum value in .

[0063] In step S3, based on the positive definite spectrum deco...

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Abstract

The invention discloses an improved Latin hypercube sampling method suitable for non-positive correlation control. The method comprises the steps of S1, obtaining the cumulative distribution function of input variables or a large amount of measured discrete data and a correlation coefficient matrix among the variables; S2, extracting samples using the importance sampling method when the distribution function is known and using the improved Latin hypercube sampling method when the distribution function is unknown, and obtaining a sample matrix; S3, modifying the correlation coefficient matrix based on the positive definite spectral decomposition method; S4, conducting the Cholesky decomposition and correlation transformation on the correlation coefficient matrix of a modified random order matrix; S5, computing an order matrix through the specified correlation coefficient matrix, and determining the final sample matrix according to the sorting of the order matrix; S6, calculating load flow after the sample matrix is brought into nodes, obtaining node voltage and branch power, and calculating the relative error index. According to the improved Latin hypercube sampling method, the problems that the distribution function of the input variables is unknown and the non-positive definite matrix cannot be decomposed are solved, and the application scope of the Latin hypercube sampling method is expanded.

Description

technical field [0001] The invention relates to a Latin hypercube sampling method, in particular to an improved Latin hypercube sampling technique suitable for non-positive deterministic correlation control in distribution network simulation calculations, and belongs to the random flow of power system distribution networks after distributed power sources are connected Sampling the field of simulation technology. Background technique [0002] Distributed power generation technology based on renewable energy such as photovoltaics and wind power can effectively alleviate problems such as energy shortage and environmental pollution, and has been widely used. However, the power flow distribution of the distribution network will be affected by the grid-connected operation of distributed power generation. The output power of photovoltaic power generation and wind power generation has strong randomness due to the influence of natural environmental factors. When distributed power s...

Claims

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Application Information

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IPC IPC(8): G06F17/50
CPCG06F30/20
Inventor 徐青山杨阳
Owner SOUTHEAST UNIV
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