基于关联挖掘的无功优化关键参数智能辨识方法

CHEN Guangyu

您当前的位置：

首页 >

文章列表页 >

基于关联挖掘的无功优化关键参数智能辨识方法

更新时间：2025-08-13

- 基于关联挖掘的无功优化关键参数智能辨识方法
- Automation of Electric Power Systems Vol. 41, Issue 23, Pages: 109-116(2017)
- 作者机构：
  
  南京工程学院电力工程学院,江苏省,南京市,211167
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2017
- 稿件说明：
移动端阅览
CHEN Guangyu. [J]. Automation of electric power systems, 2017, 41(23): 109-116.
DOI：

CHEN Guangyu. [J]. Automation of electric power systems, 2017, 41(23): 109-116. DOI：

摘要

针对传统无功优化中关键参数设置过程繁琐且设置结果不合理的问题，首先，给出一种基于斜率分段归并的曲线划分策略，用于对预测区间进行智能划分；其次，采用一种标准化欧式距离—动态时间弯曲(ED-DTW)混合策略，用于不同数据集间相似度的计算；最后，提出一种基于数据关联挖掘的无功优化参数智能辨识框架，用于对数据库内的历史数据进行挖掘。仿真采用实际电网数据对整个挖掘过程进行分析，挖掘结果显示，提出的辨识框架能自动给出参数的时段划分和设置结果，将挖掘得到的参数结果用于实际控制中，表明该方法获得的划分结果符合负荷峰谷特征，且相比传统方法，在减小电压偏差和提高电压合格率上效果更好。

Abstract

Owing to the problems of tedious setting process of key parameter and unreasonable setting results during the performing of traditional reactive power optimization

firstly

a curve division strategy based on slope segment merging is presented to divide the forecasting interval intelligently. Secondly

a hybrid strategy of standard Euclidean distance-dynamic time warping(ED-DTW)is used to calculate the similar distance of different data sets. Finally

an intelligent identifying framework of reactive power optimization parameter based on data mining association is proposed to mine the data in the database. The simulation is analyzed by adopting real grid data. The mining results show that the parameter identifying framework could automatically give parameter time division and setting results. The mining parameter used in real control shows that the calculation speed of this approach is fast and has better effect on reducing voltage deviation and improving qualified voltage rate.

关键词

Keywords

references

Views

2432

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Ultra-short-term Load Forecasting Considering Missing Data in Anomalous Scenarios

Dynamic Reconfiguration Method for Distribution Network Based on Decision-oriented Period Division

Online Reactive Power Optimization Method Based on Data-driven Predictive Control for Distribution Network with Low Situation Awareness

Bi-level Energy Scheduling Strategy for Hydrogen-powered Ships Combined with Multi-step Load Forecasting

Related Author

LI Jiawen

SUN Yonghui

WANG Sen

ZHANG Ziwei

WANG Yao

ZHOU Guoxun

YU Tao

LIU Qianjin

Related Institution

College of Artificial Intelligence and Automation, Hohai University

School of Applied Technology, Nanjing Institute of Technology

School of Electric Power, South China University of Technology

Guangdong Provincial Key Laboratory of Intelligent Measurement and Advanced Metering of Power Grid

School of Electrical Engineering, Zhejiang University

⁰