华南理工大学学报(自然科学版) ›› 2011, Vol. 39 ›› Issue (3): 62-66.doi: 10.3969/j.issn.1000-565X.2011.03.013

• 动力与电气工程 • 上一篇    下一篇

基于AHP灰关联及GM(1,N)建模的静态网损因素分析

张勇军1 石辉1 梁锦照2  韩鹏1   

  1. 1. 华南理工大学 电力学院∥广东省绿色能源技术重点实验室,广东 广州 510640; 2. 广东电网公司 中山供电局,广东 中山 528400
  • 收稿日期:2010-04-21 修回日期:2010-08-27 出版日期:2011-03-25 发布日期:2011-02-01
  • 通信作者: 张勇军(1973-) ,男,博士,副教授,主要从事电力系统无功优化和电压稳定、电力系统可靠性与规划等研究 E-mail:zhangjun@ scut.edu.cn
  • 作者简介:张勇军(1973-) ,男,博士,副教授,主要从事电力系统无功优化和电压稳定、电力系统可靠性与规划等研究
  • 基金资助:

    国家自然科学基金重点资助项目( 50337010)

Analysis of Static Loss Factors Based on AHP-Gray Relation and GM(1,N) Model

Zhang Yong-jun1  Shi Hui1  Liang Jin-zhao2  Han Peng1   

  1. 1. South China university of technology, guangdong province electricity institute ∥ green energy technology key laboratory, guangdong guangzhou 510640; 2. Guangdong power grid corporation power supply bureau zhongshan, guangdong zhongshan 528400
  • Received:2010-04-21 Revised:2010-08-27 Online:2011-03-25 Published:2011-02-01
  • Contact: 张勇军(1973-) ,男,博士,副教授,主要从事电力系统无功优化和电压稳定、电力系统可靠性与规划等研究 E-mail:zhangjun@ scut.edu.cn
  • About author:张勇军(1973-) ,男,博士,副教授,主要从事电力系统无功优化和电压稳定、电力系统可靠性与规划等研究
  • Supported by:

    国家自然科学基金重点资助项目( 50337010)

摘要: 提出利用变权灰关联技术分析电网静态网损因素与线损率指标之间关联度的思路,并建立GM(1,N)线损率预测模型.首先根据线损机理确定各因素对线损影响的权重,而后借助灰关联原理比较各因素与线损率的关联度,最后优选强关联因素建立线损率预测模型,用于配网线损率预测及相关因素指标优化的降损潜力分析.算例表明,灰关联分析准确,模型预测精度高.该分析方法所需样本少,计算便捷,适用于指导电网节能评估及规划.

关键词: 灰关联, 变权, GM(1, N), 网损因素, 线损率

Abstract:

Proposed in this paper is an idea of analyzing the correlation degree between static loss factors and line loss rate through gray relation(GR) with variable weights,and a GM(1,N) model for predicting the line loss rate.In this method,first,the impact weights of various factors on the line loss are determined based on the loss mechanism.Then,the relationships between various factors and the line loss rate are compared via the GR method.Finally,strongly-correlated factors are picked to establish a loss-rate model for the prediction of distribution line loss rate and loss reduction potential due to relevant factor optimization.Case analyses show that both the GR analysis and the prediction is of high accuracy,and that the proposed method requires fewer samples and less computation,so that it provides a useful guidance for the energy-saving evaluation and planning of power network.

Key words: gray relation, variable weights, GM(1, N), loss factor, line loss rate