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基于改进PSO算法的短期发电计划研究 被引量:11

Improved PSO algorithm and its application in short-term generation scheduling
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摘要 介绍了粒子群优化算法PSO(Particle Swarm Optimization),并针对短期发电计划中的优化问题提出了一种改进PSO算法,将表示机组开停机状态的离散变量转换为0~1范围内的连续变量,与机组出力一起进行PSO优化搜索,然后再利用就近取整函数'round'将其转换成整数变量.详细描述了应用改进PSO算法求解机组优化启停问题的具体步骤.将该方法应用于10机系统,实验结果表明该改进PSO算法用于短期发电计划是可行的. PSO(Particle Swarm Optimization) algorithm is introduced, and an improved PSO algorithm is proposed for the optimization of short-term generation scheduling . The discrete variables representing the unit status are transformed to continuous variables from zero to one, which together with unit output, are optimized by PSO and finally transformed to integral variables using function'round'. Its implementation steps are detailed. A ten-machine system is tested with the proposed method, and the results show its feasibility.
出处 《电力自动化设备》 EI CSCD 北大核心 2005年第3期34-37,40,共5页 Electric Power Automation Equipment
关键词 电力系统 粒子群算法 短期发电计划 power system particle swarm optimization short-term generation scheduling
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参考文献12

  • 1王承民,郭志忠,于尔铿.确定机组组合的一种改进的动态规划方法[J].电网技术,2001,25(5):20-24. 被引量:29
  • 2周驰,高海兵,高亮,章万国.粒子群优化算法[J].计算机应用研究,2003,20(12):7-11. 被引量:177
  • 3HABIBOLLAHZADEH H,BUBENKO J A. Application of decomposition techniques to short-term operation planning of hydrothermal power system[J]. IEEE Trans on Power Systems, 1986,1 ( 1 ) :41 - 47. 被引量:1
  • 4ZHANG F,GALIANA F D. Unit commitment by simulated annealing[J]. IEEE Trans on Power Systems,1990,5 (1):311-318. 被引量:1
  • 5KAZARLIS S A,BAKIRTZIS A G,PETRIDIS V. A genetic algorithm solution to the unit commitment problem[J].IEEE Trans on Power Systems,1996,11(1):83-92. 被引量:1
  • 6RAJAN C C A,MOHAN M R,MANIVANNAN K. Neural based Tabu search method for solving unit commitment problem[A]. Power System Management and Control[C]. [s. l.]:[s, n.],2002. 180-185. 被引量:1
  • 7SASAKI H,WATANABE M,KUBOKAMA J,etal. A solution method of the unit commitment by artificial neural networks[J]. IEEE Trans on Power Systems,1992,7 (3) :974-981. 被引量:1
  • 8LI S,SHAHIDEHPOUR S M,WANG C. Promoting the application of experts systems in short- term unit commitment[J]. IEEE Trans on Applied Superconductivity,1993,3( 1 ) :286- 292. 被引量:1
  • 9OUYANG Z,SHAHIDEHPOUR S M. A hybrid artificial neural network-dynamic programming approach to unit commitment[J]. IEEE Trans on Power Systmns,1992,7( 1 ) :236 - 242. 被引量:1
  • 10KENNEDY J,EBERHART R C. Particle swarm optimization[A]. Proceedings of IEEE International Conference on Neutral Networks[C]. Perth,Australia: [s.n.],1995.1942 - 1948. 被引量:1

二级参考文献32

  • 1[1]Kennedy J, Eberhart RC,Shi Y.Swarm Intelligence[M].San Francisco:Morgan Kaufman Publishers,2001. 被引量:1
  • 2[2]Mataric M.Designing and Understanding Adaptive Group Behavior[J].Adaptive Behavior,1995,4:1-12. 被引量:1
  • 3[3]Dorigo M,V Maniezzo,A Colorni.The Ant System:Optimization by a Colony of Cooperating Agents[J].IEEE Transactions on Systems, Man and Cybernetics, 1996. 被引量:1
  • 4[4]Kennedy J,Eberhart R C.Particle Swarm Optimization[C].Proceedings of IEEE International Conference on Neutral Networks,Perth,Australia,1995.1942-1948. 被引量:1
  • 5[5]Kennedy J.The Particle Swarm:Social Adaptation of Knowledge[C].Proceedings of IEEE International Conference on Evolutionary Computation,Indianapolis,Indiana,1997. 被引量:1
  • 6[6]Eberhart R C,Kennedy J.A New Optimizer Using Particle Swarm Theory[C].Proceedings of Sixth International Symposium Micro Machine and Human Science,Nagoya,Japan,1995. 被引量:1
  • 7[7]Shi Y H,Eberhart R C.Parameter Selection in Particle Swarm Optimization[C].Annual,1998. 被引量:1
  • 8[8]Eberhart R C, Shi Y H.Comparison between Genetic Algorithms and Particle Swarm Optimization[R].Annual Conference on Evolutionary Programming, San Diego,1998. 被引量:1
  • 9[9]Shi Y H,Eberhart R C.A Modified Particle Swarm Optimizer[R].IEEE International Conference on Evolutionary Computation,Anchorage,Alaska,1998. 被引量:1
  • 10[10]Shi Y H,et al.Empirical Study of Particle Swarm Optimization[R].Proceedings of Congress on Evolutionary Computation,1999. 被引量:1

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