适用于光伏多峰功率跟踪的改进型粒子群优化算法

胡克用 1; 2; 胥芳 1; 艾青林 1; 欧阳静 1; 徐红伟 1

doi:10.7652/xjtuxb201504023

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适用于光伏多峰功率跟踪的改进型粒子群优化算法

更新时间：2025-07-09

- 适用于光伏多峰功率跟踪的改进型粒子群优化算法
- Improved Particle Swarm Optimization for Photovoltaic Multi-Peak Power Tracking
- 西安交通大学学报 2015年49卷第4期页码：140-148
- 作者机构：
  
  1. 浙江工业大学特种装备制造与先进加工技术教育部/浙江省重点实验室,杭州,310014
  2. 杭州师范大学钱江学院,杭州,310036
- 作者简介：
- 基金信息：
  
  : 2014-09-01。作者简介: 胡克用(1978—),男,讲师;胥芳(通信作者),女,教授,博士生导师。基金项目: 国家“863计划”资助项目(2013AA050405);国家自然科学基金资助项目(51275470);教育部高等学校博士学科点专项科研基金资助项目(2013ZA11086)
- DOI：10.7652/xjtuxb201504023
  中图分类号： TK513.4
- 网络首发：2015-04-10，
  
  纸质出版：2015
- 稿件说明：
移动端阅览
胡克用 1, 2, 胥芳 1, 等. 适用于光伏多峰功率跟踪的改进型粒子群优化算法[J]. 西安交通大学学报, 2015,49(4):140-148.

Improved Particle Swarm Optimization for Photovoltaic Multi-Peak Power Tracking[J]. 2015, 49(4): 140-148.
胡克用 1, 2, 胥芳 1, 等. 适用于光伏多峰功率跟踪的改进型粒子群优化算法[J]. 西安交通大学学报, 2015,49(4):140-148. DOI： 10.7652/xjtuxb201504023.

Improved Particle Swarm Optimization for Photovoltaic Multi-Peak Power Tracking[J]. 2015, 49(4): 140-148. DOI： 10.7652/xjtuxb201504023.

摘要

针对在自然环境下光伏阵列上时常发生的局部阴影而引起P-V曲线由单峰转变成多峰状态

从而导致常规最大功率跟踪算法失效的问题

在研究传统粒子群算法的基础上

提出了一种改进型控制算法。该算法采用全局模式和局部模式两种运行手段定位最大峰值点

在对粒子群优化的速度更新方式上

去除了大量的随机变量干扰

使结构优化非常明显。改进后粒子群优化算法能够使功率跟踪避免陷入局部最优

使之找到真正的最大功率点。通过与传统粒子群算法对比仿真及试验

结果表明

在光伏阵列局部遮荫的情况下

改进后的粒子群优化算法可以快速准确地搜索到最大功率点

追踪精度高达95%

并且比传统的粒子群算法在搜索效率上提升28%

较好地避免了陷入局部最优。

Abstract

The partial shade on photovoltaic array appears repeatedly in natural environment to change single peak into multi-peaks in P-V curve. An improved algorithm following the traditional particle swarm optimization is proposed

where the global mode and local mode are adopted to locate the maximum power point. To accelerate the speed of particle swarm optimization

a lot of random and interfered variables are removed to realize an obvious structure optimization. The improved particle swarm optimization algorithm prevents power tracking from falling into the local optimum

and finds the true maximum power point. Simulation and test show that for partial shading

the improved algorithm can accurately and quickly search out the maximum power tracking point with 95% tracking accuracy

and searching efficiency is 28% higher than that of the traditional particle swarm algorithm. Especially

the local optimum can be avoided.

关键词

Keywords

references

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