Application of Small World Network PSO Algorithm in Power System

唐京瑞; 毕贵红; 王曦

您当前的位置：

首页 >

文章列表页 >

Application of Small World Network PSO Algorithm in Power System

更新时间：2026-09-14

- Application of Small World Network PSO Algorithm in Power System
- 基于小世界网络的PSO算法在电力系统中的应用
- Vol. 33, Issue 2, Pages: 135-140(2012)
- 作者机构：
  
  昆明理工大学电力工程学院
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2012
- 稿件说明：
移动端阅览
唐京瑞, 毕贵红, 王曦. Application of Small World Network PSO Algorithm in Power System[J]. 2012, 33(2): 135-140.
DOI：

唐京瑞, 毕贵红, 王曦. Application of Small World Network PSO Algorithm in Power System[J]. 2012, 33(2): 135-140. DOI：

摘要

针对发电机的阀点效应

电力系统机组优化组合属于复杂的具有混合型变量的组合优化问题

基于小世界网络(Small World Network

SWN)的邻域模型构造

最优化原理和基本粒子群算法(ParticleSwarm Optimization

PSO)

以24个时间段所有机组总耗量最小为目标函数

建立了发电机组优化组合的数学模型.提出了将"平均最短路径小

聚集系数大"的小世界网络邻域结构引入到粒子群算法中

以机组的输出功率作为粒子的位置

给出了算法的具体实现方法.在10机系统中分别采用了SWN-PSO算法和遗传算法进行了仿真计算.算例结果表明:所提出的算法不仅有利于粒子之间的信息共享

并且可以更快、更准确地收敛到全局最优解

具有一定的实用性.

Abstract

In terms of valve-point effect of the conventional generators

unit commitment is a complex combination of variables with mixed-type combinatorial optimization problems.Based on small-world network(SWN) model of the neighborhood structure and particle swarm optimization(PSO)

by taking with the minimum total consumption of all the units within 24 time periods as the objective function of

the mathematical model of optimal unit was established

which introduced the small world network model

featuring small average shortest path and clustering coefficient large

into PSO neighborhood structure.By taking the output power as the particle position

both SWN-PSO algorithm and genetic algorithm simulation were applied in a 10 unites system.The simulation results show that the proposed algorithm is conducive to information sharing between the particles

but also can converge faster and more accurately to the global optimal solution.

关键词

Keywords

references

Wang S J.Short-term generation scheduling withtransmission and environmental constraints using anaugmented lagrangian relaxation[J].IEEE Trans onPower Systems,1995,10(3):1294-1301.

国博.改进粒子群算法在高维复杂函数寻优中的应用[J].计算机工程与应用,2010,46(16):232-234.Guo Bo.Improved particle swarm optimizationalgorithm for high-dimension complex functions[J].Computer Engineering and Applications,2010,46(16):232-234.(in Chinese)

张洋,罗可.基于改进粒子群算法的聚类算法[J].计算机工程与应用,2009,45(33):132-134.Zhang Yang,Luo Ke.Clustering method based onimproved particle swarm optimization[J].ComputerEngineering and Applications,2009,45(33):132-134.(in Chinese)

陈皓勇,王锡凡.机组组合问题的优化方法综述[J].电力系统自动化,1999,23(4):51-54.Chen Haoyong,Wang Xifan.A survey of optimizationbased metheods for unit commitment[J].Automationof Electric Power Systems,1999,23(4):51-54.(inChinese)

蔡超豪,蔡元宇.机组优化组合的遗传算法[J].电网技术,1997,21(1):44-46.Cai Chaohao,Cai Yuanyu.Optimization of unitcommitment by genetic algorithm[J].Power SystemTechnology,1997,21(1):44-46.(in Chinese)

夏小翔.基于元胞机的小生境微粒群算法[J].计算机工程与应用,2007,43(11):66.Xia Xiaoxiang.Niche PSO algorithm based on CA[J].Computer Engineering and Applications,2007,43(11):66.(in Chinese)

纪震,廖惠连,吴青华.粒子群算法及应用[M].北京:科学出版社,2009:16-18.

Kennedy J,Eberhart R.Particle swarms optimization[C].Proc IEEE Int Conf on Neural Net Works.Perth,1995:1942-1948.

文劲宇.遗传算法及其在电力系统中的应用(上、下)[J].电力系统自动化,1996(10):57.Wen Jinyu.Genetic algorithm and its applications topower systems[J].Automation of Electric PowerSystems,1996(10):57.(in Chinese)

Liang J J,Suganthan P N.Dynamic multi-swarmparticle swarm optimizer[C].Proceedings of theIEEE International Swarm Intelligence Symposium,2005:124-128.

Cao P B,Xiao R B.Assembly planning using a novelimmune approach[J].International Journal ofAdvanced Manufacturing Technology,2007,31(7):770-779.

Eberhart R,Shi Y.Particle swarm optimization[C].Piscataway:Applications and Resources,Pro-ceedings of IEEE Congress on EvolutionaryComputation,2001:81-87.

蒲维,滕欢.改进PSO算法解决电力系统机组优化组合问题[J].四川电力技术,2007,30(3):16-17.Pu Wei,Teng Huan.Improved PSO mgofithm forunit commitment optimization of power system[J].Sichuan Electric Power Technology,2007,30(3):16-17.(in Chinese)

穆华平.基于动态邻域结构的微粒群算法研究[D].太原:太原科技大学,2008:7-26.

强金龙,于尔铿.电力系统经济调度[M].哈尔滨:哈尔滨工业大学出版社,1993:99-103.

Newan M E J,Watts D J.Renormalization groupanalysis of the small-world network model[J].Physics.Letter.A,1999,263:341-347.

魏萍.基于遗传算法的电力系统日有功优化调度[J].电力系统自动化,1999,23(10):23-26.Wei Ping.Daily generation scheduling based ongenetic algorithm[J].Automation of Electric PowerSystems,1999,23(10):23-26.(in Chinese)

骆济寿,张川.电力系统优化运行[M].武汉:华中理工大学出版社,1990:101-103.

牛奔,李丽,楚湘华.新型多群体协同进化粒子群优化算法[J].计算机工程与应用,2009,45(3):28-29.Niu Ben,Li Li,Chu Xianghua.Novel multi-swarmcooperative particle swarm optimization[J].Computer Engineering and Applications,2009,45(3):28-29.(in Chinese)

Views

143

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Study on Hybrid MPPT Algorithm of Zoning Disturbance Observation and Particle Swarm Optimization

Design of Parallel Robot Experimental Platform Based on Reconfigurable Innovative Components

A Meta⁃Learning and VAE⁃GAN Based META⁃VAEGAN⁃DNN Method for Network Intrusion Detection

Hopkinson Bar Pulse Width Extension Test Method Based on Polyurethane Wave Shaper

Network Intrusion Detection Model Based on Recurrence Plot and MobileViT⁃AESTF

Related Author

李简

任一峰

Related Institution

中北大学电气与控制工程学院

⁰