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Importance sampling Monte Carlo power system reliability evaluation method based on geometric optimization-minimum variance method

A technology of geometric optimization and power system, applied in the direction of instruments, data processing applications, resources, etc., can solve problems such as the difficulty of solving the variance minimization model

Active Publication Date: 2016-10-12
XI AN JIAOTONG UNIV
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Problems solved by technology

However, the variance minimization model is generally difficult to solve, so it is difficult to solve this problem reasonably

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  • Importance sampling Monte Carlo power system reliability evaluation method based on geometric optimization-minimum variance method
  • Importance sampling Monte Carlo power system reliability evaluation method based on geometric optimization-minimum variance method
  • Importance sampling Monte Carlo power system reliability evaluation method based on geometric optimization-minimum variance method

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[0040] The present invention will be described in detail below in conjunction with the accompanying drawings and embodiments.

[0041] The present invention is an important sampling reliability evaluation method based on geometric optimization to solve the variance minimum model, the method uses geometric optimization (GP) to efficiently solve the variance minimization (VM) model in the reliability evaluation, and obtains the required important sampling parameters, These parameters are then used for associated reliability assessments. Especially for those highly reliable systems to carry out reliability assessment. It is specifically divided into two stages: using geometric optimization to solve the variance minimum model in the pre-sampling, so as to solve the reliability evaluation parameters, and performing reliability evaluation in the main sampling:

[0042] Level 1 pre-sampling (solution parameters): first pre-sampling generates initial samples, and then based on these ...

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Abstract

The invention discloses an importance sampling Monte Carlo power system reliability evaluation method based on a geometric optimization-minimum variance method. First, a geometric optimization method in the field of planning and a variance minimization model in importance sampling are combined together, and a variance minimization model in reliability evaluation is solved through geometrical optimization, and then, reliability evaluation is conducted using obtained importance sampling parameters. Compared with the traditional reliability evaluation method based on importance sampling, the reliability evaluation method of the invention has the characteristics of smaller convergence variance, faster convergence and higher evaluation precision. By improving the evaluation speed and evaluation precision of system reliability evaluation and especially by capturing small-probability / highly-influential rare events, the reliability nuance between different power grid planning schemes can be evaluated quickly, and a quantitative and accurate auxiliary reference basis can be provided for the selection of power grid planning schemes.

Description

technical field [0001] The invention belongs to the field of power system planning and evaluation, and relates to an important sampling Monte Carlo power system reliability evaluation method based on a geometric optimization-minimum variance method that can rapidly increase the speed of reliability evaluation and consider rare events. Background technique [0002] In recent years, due to the increasingly strong grid structure of the power system, people have paid more and more attention to extreme events in the power system, and these extreme low-probability / high-impact events are generally difficult to sample. In the face of these events, traditional reliability assessment methods often Appears too weak. On the other hand, the calculation of these low-probability events is very meaningful for the planning of the power system. Since the current grid structure is generally very strong, it is particularly important to be able to accurately calculate the differences in reliabil...

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Application Information

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IPC IPC(8): G06Q10/06G06Q50/06
CPCG06Q10/06393G06Q50/06
Inventor 别朝红严超丁涛王灿胡源
Owner XI AN JIAOTONG UNIV
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