Combined cumulant and Gaussian mixture approximation for correlated probabilistic load flow studies: a new approach

B. Rajanarayan Prusty; Debashisha Jena

doi:10.17775/CSEEJPES.2016.00024

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Combined cumulant and Gaussian mixture approximation for correlated probabilistic load flow studies: a new approach

更新时间：2025-12-18

- Combined cumulant and Gaussian mixture approximation for correlated probabilistic load flow studies: a new approach
- Combined cumulant and Gaussian mixture approximation for correlated probabilistic load flow studies: a new approach
- 中国电机工程学会电力与能源系统学报(英文) 2016年2卷第2期页码：71-78
- 作者机构：
  
  National Institute of Technology Calicut, Calicut, Kerala,IN
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2016.00024
  中图分类号：
- 纸质出版：2016
- 稿件说明：
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B. Rajanarayan Prusty, Debashisha Jena. Combined cumulant and Gaussian mixture approximation for correlated probabilistic load flow studies: a new approach[J]. 中国电机工程学会电力与能源系统学报(英文), 2016,2(2):71-78.

B. Rajanarayan Prusty, Debashisha Jena. Combined cumulant and Gaussian mixture approximation for correlated probabilistic load flow studies: a new approach[J]. CSEE Journal of Power and Energy Systems, 2016, 2(2): 71-78.
B. Rajanarayan Prusty, Debashisha Jena. Combined cumulant and Gaussian mixture approximation for correlated probabilistic load flow studies: a new approach[J]. 中国电机工程学会电力与能源系统学报(英文), 2016,2(2):71-78. DOI： 10.17775/CSEEJPES.2016.00024.

B. Rajanarayan Prusty, Debashisha Jena. Combined cumulant and Gaussian mixture approximation for correlated probabilistic load flow studies: a new approach[J]. CSEE Journal of Power and Energy Systems, 2016, 2(2): 71-78. DOI： 10.17775/CSEEJPES.2016.00024.

摘要

Abstract

In this paper

a probabilistic load flow analysis technique that combines the cumulant method and Gaussian mixture approximation method is proposed. This technique overcomes the incapability of the existing series expansion methods to approximate multimodal probability distributions. A mix of Gaussian

non-Gaussian

and discrete type probability distributions for input bus powers is considered. Probability distributions of multimodal bus voltages and line power flows pertaining to these inputs are precisely obtained without using any series expansion method. At the same time

multiple input correlations are considered. Performance of the proposed method is demonstrated in IEEE 14 and 57 bus test systems. Results are compared with cumulant and Gram Charlier expansion

cumulant and Cornish Fisher expansion

dependent discrete convolution

and Monte Carlo simulation. Effects of different correlation cases on distribution of bus voltages and line power flows are also studied.

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