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基于仿射最小路法的含分布式电源配电网可靠性分析

熊小萍 杨露 李宁 李正天

电力系统自动化2017,Vol.41Issue(17):43-50,8.
电力系统自动化2017,Vol.41Issue(17):43-50,8.DOI:10.7500/AEPS20161223003

基于仿射最小路法的含分布式电源配电网可靠性分析

Reliability Analysis of Distribution Network with Distributed Generators Based on Affine Minimal Path Method

熊小萍 1杨露 1李宁 1李正天2

作者信息

  • 1. 广西大学电气工程学院, 广西壮族自治区南宁市 530004
  • 2. 华中科技大学电气与电子工程学院, 湖北省武汉市 430074
  • 折叠

摘要

Abstract

Wind power and photovoltaic generation in large scale is connected to the distribution network in the form of distributed power supply, which increases the complexity in the reliability assessment of the distribution network.A multi-capacity state model of the generator based on the Markov process is developed to simulate the output fluctuation in face of the randomness of the distributed power output.In order to avoid the over-estimation problem of the interval minimum method, an affine minimal path method is proposed to effectively reduce the range of calculation results.This algorithm considers not only the original parameter uncertainties for components and the load points in the distribution network, but also the correlation between the uncertain variables.With the IEEE-RBTS BUS 6 power distribution system as an example, the rationality of the proposed model and algorithm is verified, and the influence of transient fault on the reliability evaluation of the system is analyzed.The result shows that the affine minimal path method has higher accuracy than the interval-minimum method in calculating the reliability index.The greater the uncertainty of the system is, the more obvious will the effect of interval reduction appear, which provides a reliable basis for reliability evaluation and system planning and design in actual engineering.

关键词

多容量状态模型/仿射最小路法/瞬时故障/分布式电源/配电网可靠性

Key words

multi-capacity state model/affine minimal path method/transient fault/distributed generator/distribution network reliability

引用本文复制引用

熊小萍,杨露,李宁,李正天..基于仿射最小路法的含分布式电源配电网可靠性分析[J].电力系统自动化,2017,41(17):43-50,8.

基金项目

国家自然科学基金资助项目(51577077).This work is supported by National Natural Science Foundation of China (No.51577077). (51577077)

电力系统自动化

OA北大核心CSCDCSTPCD

1000-1026

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