[1]邹恩,辛建涛,林兰,等.修正的混沌粒子群算法求解经济负荷分配[J].江西师范大学学报(自然科学版),2013,(05):482-487.
 ZOU En,XIN Jian-tao,LIN Lan,et al.The Modified Chaotic Particle Swarm Optimization Algorithm in the Economic Load Dispatch[J].,2013,(05):482-487.
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修正的混沌粒子群算法求解经济负荷分配()
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《江西师范大学学报》(自然科学版)[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2013年05期
页码:
482-487
栏目:
出版日期:
2013-10-31

文章信息/Info

Title:
The Modified Chaotic Particle Swarm Optimization Algorithm in the Economic Load Dispatch
作者:
邹恩;辛建涛;林兰;龚昕;林锦钱
华南农业大学工程学院,广东广州,510642;深圳市广和通实业发展有限公司,广东深圳,518057;随州供电公司变电中心,湖北随州,441300
Author(s):
ZOU En;XIN Jian-tao;LIN Lan;GONG Xin;LIN Jin-qian
关键词:
混沌优化粒子群优化电力系统经济负荷分配
Keywords:
Chaos optimizationparticle swarm optimizationpower systemeconomic load dispatch
分类号:
TM714
文献标志码:
A
摘要:
为克服粒子群优化算法容易陷入局部最优、后期收敛慢等缺点,提出了一种修正的混沌粒子群优化算法.该算法通过修正粒子群迭代的行动策略,并引入遍历性较强的Tent混沌局部搜索机制,可以增强粒子的全局搜索能力,提高优化算法的全局寻优性能.将修正的混沌粒子群算法分别应用于6机组和15机组电力系统中求解经济负荷分配,在考虑系统网损和机组运行约束条件的情况下进行仿真实验.仿真结果表明:该算法用于求解高维、非凸、不连续等非线性复杂约束条件的电力系统经济负荷分配问题上,有着较快的收敛速度和较强的全局寻优能力.最后,通过与其它智能算法比较,验证了算法的有效性和优越性.
Abstract:
A modified particle swarm optimization algorithm was presented in order to overcome the weakness of the particle swarm algorithm which has slown convergence rate and is easily trapped in local optimum.The global optimal performance of optimization algorithm was improved by revising the iterative strategy of the particle swarm and introducing the local search mechanism by Tent chaotic map which has strong ergodicity to enhance the global searching of particles.The modified chaotic particle swarm optimization was applied to the simulation in economic load dispatch of 6 unit and 15unit power system respectively,considering the transmission network losses and constrained conditions of the units operation.The results of the simulation show that the algorithm has a faster constringency rate and better global optimization in solving the economic load dispatch problems in power systems,which were of complex constraints such as:high dimension,nonlinear,non-convex,and discrete characteristics etc.Finally,it proves the effectiveness and superiority of this algorithm compared with the other intelligence algorithms.

参考文献/References:

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备注/Memo

备注/Memo:
国家自然科学基金(31171457);广东省自然科学基金(S2013040016144);广东省产学研结合基金(2010B090400451,201213091100020)
更新日期/Last Update: 1900-01-01