共查询到10条相似文献,搜索用时 125 毫秒
1.
提出了一种粒子群算法与遗传算法结合的组合粒子群算法,并将其用于求解复杂的、非线性的水火电混合电力系统电源规划问题。该结合算法引入的遗传算法成功地提高了基本粒子群算法的全局搜索能力,同时也比基本遗传算法的收敛速度更快。算例结果表明:对于短期规划,该算法能可靠、快速地收敛到全局最优解,对于大型电力系统的中长期电源规划问题也可得到较好解。 相似文献
2.
This paper presents a particle swarm optimization (PSO) based approach to solve the multi-stage transmission expansion planning problem in a competitive pool-based electricity market. It is a large-scale non-linear combinatorial problem. We have considered some aspects in our modeling including a multi-year time horizon, a number of scenarios based on the future demands of system, investment and operating costs, the N − 1 reliability criterion, and the continuous non-linear functions of market-driven generator offers and demand bids. Also the optimal expansion plan to maximize the cumulative social welfare among the multi-year horizon is searched. Our proposed PSO based approach, namely modified PSO (MPSO), uses a diversity controlled PSO to overcome the problem of premature convergence in basic PSO (BPSO) plus an initial high diversity swarm to cover the search space efficiently. The MPSO model is applied to the Garver six-bus system and to the IEEE 24-bus test system and compared to the BPSO model and a genetic algorithm based model. 相似文献
3.
E. Nasr Azadani S. H. Hosseinian P. Hasanpor 《Electrical Engineering (Archiv fur Elektrotechnik)》2008,90(7):503-510
This paper proposes an approach for optimal placement of STATic synchronous COMpensator (STATCOM) in power systems. The approach
is based on the simultaneous application of particle swarm optimization (PSO) and continuation power flow (CPF) to improve
voltage profile, minimizing power system total losses, and for maximizing system loadability with respect to the size of STATCOM.
Simulation results show the suitability of the PSO technique in finding multiple optimal solutions to the problem with reasonable
computational effort. The installation of the STATOCM on these buses can increase the system voltage stability margin. The
proposed technique is examined on the IEEE57 bus test system. 相似文献
4.
粒子群优化(PSO)算法是一种新兴的群体智能优化技术,其思想来源于人工生命和演化计算理论,PSO通过粒子追随自己找到的最优解和整个群的最优解来完成优化。该算法简单易实现,可调参数少,已得到广泛研究和应用。在大量参阅国内外相关文献的基础上,简要介绍了PSO算法的工作原理,较为全面地详述了粒子群优化方法在电力系统中的应用,如电网规划、检修计划、短期发电计划、机组组合、负荷频率控制、最优潮流、无功优化、谐波分析与电容器配置、参数辨识、状态估计、优化设计等方面,并对今后可能的应用指出了研究方向。 相似文献
5.
基于改进微粒群算法的水火电力系统短期发电计划优化 总被引:21,自引:3,他引:18
微粒群算法(PSO)来源于对社会模型的模拟,是一种随机全局优化技术。该算法简单,容易实现,且功能强大。中对PSO进行了改进,引入了“分群”和“灾变”思想,并应用于求解水火电力系统的短期有功负荷最优分配问题。通过具体算例验证了改进PSO算法的有效性,而且其收敛速度比遗传算法(GA)快,求解精度比普通的PSO和GA的高。 相似文献
6.
A hybrid multi-agent based particle swarm optimization algorithm for economic power dispatch 总被引:1,自引:0,他引:1
Rajesh Kumar Devendra Sharma Abhinav Sadu 《International Journal of Electrical Power & Energy Systems》2011,33(1):115-123
This paper presents a new multi-agent based hybrid particle swarm optimization technique (HMAPSO) applied to the economic power dispatch. The earlier PSO suffers from tuning of variables, randomness and uniqueness of solution. The algorithm integrates the deterministic search, the Multi-agent system (MAS), the particle swarm optimization (PSO) algorithm and the bee decision-making process. Thus making use of deterministic search, multi-agent and bee PSO, the HMAPSO realizes the purpose of optimization. The economic power dispatch problem is a non-linear constrained optimization problem. Classical optimization techniques like direct search and gradient methods fails to give the global optimum solution. Other Evolutionary algorithms provide only a good enough solution. To show the capability, the proposed algorithm is applied to two cases 13 and 40 generators, respectively. The results show that this algorithm is more accurate and robust in finding the global optimum than its counterparts. 相似文献
7.
在分析支持向量机SVM(Support VectorM ach ine)回归估计方法参数性能的基础上,提出粒子群算法PSO(Partic le Swarm Optim ization)优化参数的SVM短期电力负荷预测模型。PSO算法是一种新型的基于群体智能的随机优化算法,简单易于实现且具有更强的全局优化能力。用所建立的负荷预测模型编制的M atlab仿真程序,对某实际电网进行了短期负荷预测,结果表明预测精度更高。 相似文献
8.
Solving economic load dispatch problems in power systems using chaotic and Gaussian particle swarm optimization approaches 总被引:3,自引:0,他引:3
Leandro dos Santos Coelho Chu-Sheng Lee 《International Journal of Electrical Power & Energy Systems》2008,30(5):297-307
The objective of the Economic Dispatch Problems (EDPs) of electric power generation is to schedule the committed generating units outputs so as to meet the required load demand at minimum operating cost while satisfying all units and system equality and inequality constraints. Recently, global optimization approaches inspired by swarm intelligence and evolutionary computation approaches have proven to be a potential alternative for the optimization of difficult EDPs. Particle swarm optimization (PSO) is a population-based stochastic algorithm driven by the simulation of a social psychological metaphor instead of the survival of the fittest individual. Inspired by the swarm intelligence and probabilities theories, this work presents the use of combining of PSO, Gaussian probability distribution functions and/or chaotic sequences. In this context, this paper proposes improved PSO approaches for solving EDPs that takes into account nonlinear generator features such as ramp-rate limits and prohibited operating zones in the power system operation. The PSO and its variants are validated for two test systems consisting of 15 and 20 thermal generation units. The proposed combined method outperforms other modern metaheuristic optimization techniques reported in the recent literature in solving for the two constrained EDPs case studies. 相似文献
9.
10.
Distribution Systems Reconfiguration using a modified particle swarm optimization algorithm 总被引:1,自引:0,他引:1
A.Y. Abdelaziz F.M. Mohammed S.F. Mekhamer M.A.L. Badr 《Electric Power Systems Research》2009,79(11):1521-1530
This paper presents the particle swarm optimization (PSO) algorithm for solving the optimal distribution system reconfiguration problem for power loss minimization. The PSO is a relatively new and powerful intelligence evolution algorithm for solving optimization problems. It is a population-based approach. The PSO is originally inspired from the social behavior of bird flocks and fish schools. The proposed PSO algorithm in this paper is introduced with some modifications such as using an inertia weight that decreases linearly during the simulation. This setting allows the PSO to explore a large area at the start of the simulation. Also, a modification in the number of iterations and the population size is presented. Comparative studies are conducted on two test distribution systems to verify the effectiveness of the proposed PSO algorithm. The obtained results are compared with those obtained using other techniques in previous work to evaluate the performance. 相似文献