模糊熵权法和CCPSO算法的含风电场电力系统多目标无功优化

陈璟华; 邱明晋; 郭经韬; 唐俊杰

doi:10.12052/gdutxb.170123

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模糊熵权法和CCPSO算法的含风电场电力系统多目标无功优化

更新时间：2026-05-27

- 模糊熵权法和CCPSO算法的含风电场电力系统多目标无功优化
- Multi-objective Reactive Power Optimization in Electric Power System with Wind Farm Based on Fuzzy Entropy Weight Method and CCPSO Algorithm
- 广东工业大学学报 2018年35卷第1期页码：35-40
- 作者机构：
  
  1. 广东工业大学　自动化学院,广东,广州,510006
  2. 中国能源建设集团广东省电力设计研究院有限公司,广东,广州,510663
- 作者简介：
  
  陈璟华(1974–)，女，副教授，博士，主要研究方向为电力系统运行与优化
- 基金信息：
  
  中央财政支持地方高校发展专项资金项目（粤财教[2016]202号）
- DOI：10.12052/gdutxb.170123
  中图分类号： TM714
- 网络首发：2018-01-09，
  
  纸质出版：2018
- 稿件说明：
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陈璟华, 邱明晋, 郭经韬, 唐俊杰. 模糊熵权法和CCPSO算法的含风电场电力系统多目标无功优化[J]. 广东工业大学学报, 2018, 35(1): 35-40.

Chen Jing-hua, Qiu Ming-jin, Guo Jing-tao, et al. Multi-objective Reactive Power Optimization in Electric Power System with Wind Farm Based on Fuzzy Entropy Weight Method and CCPSO Algorithm[J]. 2018, 35(1): 35-40.
陈璟华, 邱明晋, 郭经韬, 唐俊杰. 模糊熵权法和CCPSO算法的含风电场电力系统多目标无功优化[J]. 广东工业大学学报, 2018, 35(1): 35-40. DOI： 10.12052/gdutxb.170123.

Chen Jing-hua, Qiu Ming-jin, Guo Jing-tao, et al. Multi-objective Reactive Power Optimization in Electric Power System with Wind Farm Based on Fuzzy Entropy Weight Method and CCPSO Algorithm[J]. 2018, 35(1): 35-40. DOI： 10.12052/gdutxb.170123.

摘要

研究了含双馈风电机组的电力系统多目标无功优化问题

分析了双馈风电机组的无功特性

将风电场作为连续的无功调节手段参与电力系统无功优化. 提出一种基于模糊商权法和组合混沌序列动态粒子群算法(CCPSO)的多目标无功优化方法. 利用模糊熵权法解决主观权值和客观权值的缺陷

建立以静态电压稳定浴度、节点电压和有功网损为目标的含风电场多目标无功优化决策模型. 针对利用传统粒子群算法进行求解时

易出现局部收敛等问题

结合Chebyshev映射和Logistic映射

在粒子初始化过程中运用一种组合混沌映射

增强初始粒子的均匀性

同时将Logistic混沌优化引入到算法寻优过程中

使算法全局寻优能力得到加强. 以IEEE14节点系统为例进行仿真实验

验证所提方法的有效性和实用性.

Abstract

A study is conducted on the reactive power optimization in distribution network of the wind farm. The reactive power characteristics of the double-fed wind turbine are analyzed

and the wind farm is used as a continuous reactive power adjustment means to participate in the reactive power optimization of the power system. A multi-objective reactive power optimization method is proposed based on fuzzy quotient and chaotic sequence dynamic particle swarm optimization algorithm. The fuzzy entropy weight method is used to solve the defects of subjective weight and objective weight

and a multi-objective reactive power optimization decision model with wind power is proposed based on static voltage stability margin

voltage deviation and active power loss. In order to solve the local convergence with traditional particle swarm algorithm

combined with Chebyshev mapping and Logistic mapping

a combinatorial chaotic mapping is used to enhance the homogeneity of the initial particles in the particle initialization

and Logistic chaos optimization is introduced to the process of algorithm optimization

strengthening the global optimization ability of the algorithm. The IEEE14-node system is used as a test case

and the result proves the feasibility and validity of the proposed method.

关键词

Keywords

references

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