采用混合高斯模型及边缘变换技术的蒙特卡洛随机潮流方法

XU Qingshan

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采用混合高斯模型及边缘变换技术的蒙特卡洛随机潮流方法

更新时间：2025-08-13

- 采用混合高斯模型及边缘变换技术的蒙特卡洛随机潮流方法
- Automation of Electric Power Systems Vol. 40, Issue 16, Pages: 23-30(2016)
- 作者机构：
  
  东南大学电气工程学院,江苏省,南京市,210096
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2016
- 稿件说明：
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XU Qingshan. [J]. Automation of electric power systems, 2016, 40(16): 23-30.
DOI：

XU Qingshan. [J]. Automation of electric power systems, 2016, 40(16): 23-30. DOI：

摘要

提出一种计及输入变量相关性的改进蒙特卡洛随机潮流方法。该方法针对系统输入变量多样性和随机性的特点

建立其混合高斯模型并根据测量数据进行参数估计。在蒙特卡洛仿真基础上

引入均匀设计抽样技术提高采样效率

并通过边缘变换和Cholesky分解产生具有相关性的输入变量样本。采用多重线性化手段在加快计算速度的同时减小潮流方程线性化引起的截断误差。对IEEE 30和IEEE 118节点系统的测试分析

验证了所提方法的准确性、快速性和有效性。

Abstract

A probabilistic load flow method considering the correlation between input variables based on improved Monte Carlo simulation(MCS)is proposed. Regarding the behaviors of diversity and randomness for variable input

the method establishes the Gaussian mixture model(GMM)for variable input and performs parameter estimation by the use of measured data. Uniform design sampling(UDS)is introduced to improve the sampling efficiency

and correlated samples are generated by marginal transformation and Cholesky decomposition. Moreover

multi-linearization is applied to reduce the truncated error as well as time consumption. The simulation results of IEEE 30-bus and IEEE 118-bus test system verify the effectiveness

accuracy and practicability of the proposed method. This work is supported by National Natural Science Foundation of China(No. 51377021)

Fundamental Research Funds for the Central Universities(No. 2242016K41064)and State Grid Corporation of China.

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