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改进遗传算法在电力系统无功优化中的应用 总被引:5,自引:5,他引:0
段金长 《电网与水力发电进展》2008,24(6):15-20
电力系统的无功优化控制,不仅能有效地降低系统的有功功率损耗,而且还可以改善电网的电压质量,对系统的安全稳定、经济运行具有非常重要意义。无功优化问题是一个含有连续变量和离散变量的混合优化问题,求解过程相当复杂,电力系统无功优化问题属于最优潮流问题的一个组成部分。探讨了求解无功优化的现代人工智能算法,总结分析了遗传算法的特点及使用情况。为提高解的质量与计算效率,对遗传算法做了改进,并将其应用于电力系统无功优化中。 相似文献
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针对目前应用于电力系统无功优化的智能算法所存在的问题,提出将免疫遗传算法应用于电力系统无功优化问题的措施。免疫遗传算法是将免疫理论和基本遗传算法各自的优点相结合,不仅具有遗传算法的搜索特性,还具有免疫算法的多机制求解多目标函数最优解的自适应特性,对“早熟”问题有所改善,收敛于全局最优。最后,以安康市某区域电力系统为例对算法进行了性能测试,提出了合理的调压措施,结果表明将免疫遗传算法应用于电力系统无功优化问题可以显著降低系统网损,改善电压质量。 相似文献
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遗传算法是一种模拟生物进化过程的优化算法,可用于求解包含离散化变量的复杂优化问题,本文将遗传算法应用于电力系统无功优化,并对常规遗传算法的编码方式、遗传算子以及终止判据等方面进行了改进,使用该文提出的算法对IEEE 6、IEEE 30节点系统进行了无功优化计算,结果表明该改进遗传算法应用于无功优化是合理可行的。 相似文献
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遗传算法在电力系统无功优化中的应用综述 总被引:1,自引:0,他引:1
遗传算法是近10年来发展的基于自然选择规律的一种优化方法,算法能成功的解决无功变量中的离散问题,避免常规数学优化方法的局部最优现象。本文阐述了简单遗传算法以及遗传算法与其它算法相结合的算法在电力系统无功优化中的应用和今后的发展方向。 相似文献
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根据电力系统无功优化问题的特点。提出了一种基于近似最优个体、学习算子和浑沌算子的改进遗传算法。传统遗传算法应用于电力系统无功优化问题,虽然收到了比较好的效果,但并没有充分利用电力系统本身的特点。而本研究所提的改进遗传算法,可以充分利用已有的信息和运行经验,从而达到在保证解的全局最优性的同时大大加快计算的速度。具体的算例表明了所提算法的正确性和有效性。 相似文献
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与超高压线路相比,特高压线路无功大量富余,会与下级电网形成很大的穿越无功,从而影响无功的分层控制,甚至威胁电力系统的安全稳定运行。常规的优化算法存在维数灾问题,即使是智能算法,也由于解空间维度大而寻优效率低下。对此,提出了一种基于逐次优化改进遗传算法,该方法利用逐次优化的思想,对传统遗传算法的寻优方式进行了改进,并将该算法应用于某实际区域大电网中求解无功规划问题。结果表明,该方法不仅有效降低了解空间的维度,且在保证算法效率的同时使寻优的效果得到较大的改善。 相似文献
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基于遗传算法的无功功率优化与控制专家系统开发 总被引:1,自引:4,他引:1
针对电力系统中无功功率优化问题建立了数学模型,利用函数连接网络将多目标问题转化为单目标无功优化问题,采用免疫遗传算法进行求解。 提出了分区分层的多变电所电压无功协调控制专家系统的设计思想。该系统具有较高优化精度的同时求解过程较为简单。 相似文献
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农村配电网无功补偿是保证电网安全、经济运行的重要手段,是降低电网损耗、提高电压质量的重要措施。基于此,提出了一种农村配电网无功补偿方法,该方法首先建立农电网无功优化规划数学模型,然后针对无功优化问题的特点,采用免疫遗传算法对数学模型进行求解。通过实际算例和结果分析,表明了所建立无功优化规划模型的合理性和免疫遗传算法的有效性。 相似文献
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This paper studies the optimization method of channel geometries for a proton exchange membrane fuel cell (PEMFC) using a genetic algorithm (GA). The channel and rib widths and channel height are selected as geometry variables. The fuel cell output power is chosen as the cost function for the optimization. In this paper, an in-house genetic algorithm is constructed, and the fuel cell output power is obtained using an interfacing program connected to a commercial computational fluid dynamics (CFD) tool, COMSOL, in a Matlab environment. The 2D PEMFC is used to calculate the performance cost function for computational time and cost. The calculated output power of the PEMFC is delivered to the in-house GA program to check for optimality. After the optimality is checked, the new geometry data is fed back to the COMSOL to calculate the PEMFC output power until the optimization process is finished. Experiments are conducted to support the optimized results using three different channel geometries: channel-to-rib width ratios of 0.5:1, 1:1, and 2:1. A full 3D PEMFC CFD model is constructed using COMSOL to support the 2D CFD optimization results. This paper shows the possibility of applying the geometry optimization process to sophisticated electrochemical reaction systems, such as a PEMFC, using a GA and a commercial CFD tool on the Matlab platform. The geometries and materials can be optimized using this approach to obtain the most efficient performance of an electrochemical system. 相似文献
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Many studies have attempted to optimize integrated Solid Oxide Fuel Cell-Gas Turbine (SOFC-GT), although different and somehow conflicting results are reported employing various algorithms. In this study, Multi-Objective Optimization (MOO) is employed to approach the optimal design of SOFC-GT considering all prevailing factors. The emphasis is placed on the evaluation of the Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) performance as two effective approaches for solving the multi-objective and non-linear optimization problems. Multi- objective optimization is carried out on two vital objectives; the electrical efficiency and the overall output power of the system. The considerable achievements are the set of optimal points that aim to identify the system optimal performance which provides a practical basis for the decision-makers to choose the appropriate target functions. For the studied conditions, the two algorithms nearly exhibit similar performance, while the PSO is faster and more efficient in terms of computational effort. The PSO appears to achieve its ultimate parameter values in fewer generations compared to the GA algorithm under the examined circumstances. It is found that the maximum power of 410 kW is accomplished employing the GA optimization method with an efficiency of 64%, while PSO method yields the maximum power of 419.19 kW at the efficiency of 58.9%. The results stress that PSO offers more satisfactory convergence and fidelity of the solution for the SOFC-GT MOO problems. 相似文献
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王志华 《电网与水力发电进展》2013,29(3):38-42
在电力系统中,由于电流互感器选择不当,故障时电流互感器可能在瞬时内承载近百倍的故障短路电流,造成电流互感器严重饱和。通过详细分析对机电式、电磁式饱和特性以及饱和程度、模拟式以及微机继电保护装置的动作特性的影响以及理论计算,探求合理的电流互感器选择方法。为解决此问题,一个可能办法是选择保护安装处最大短路电流与其电流互感器的饱和倍数来选择,同时增强电流互感器的抗饱和能力,合理选择继电保护装置和电流互感器,以保证在最大短路电流下不致饱和。提出了提高继电保护装置在电流互感器饱和时测量动作正确性的对策。 相似文献
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冯兴明 《电网与水力发电进展》2013,29(2):38-42
电力系统无功优化是带有多约束的非线性组合优化问题,进化策略算法在解决这类问题时显示出独特的优势。惩罚函数法是进化算法求解约束问题最常用的方法,但其罚系数难以合理确定。文中将带随机排序策略的进化算法应用到无功优化问题中,有效地避免了罚函数法处理约束问题时罚系数难以确定的缺点,并在编码方法、进化终止判据方面做了改进,有效地提高了算法的求解效率。算例证明了改进算法的有效性。 相似文献
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In order to exploit renewable energies from tidal stream, tandem propellers of a unique counter-rotating type horizontal-axis tidal turbine was firstly designed based on the blade element momentum (BEM) theory. And then a multi-objective numerical optimization method coupled the response surface method (RSM) with the genetic algorithm (GA) was employed to obtain desirable blade profiles. The front blade pitch angle distribution was taken as optimization variable in this paper, as it plays an important role in affecting the inlet conditions of the rear blade. The numerical results show that both optimization objectives of power coefficient and thrust coefficient can be significantly improved. It was verified that the performance of the power unit with the optimized blades increases obviously by optimizing the pitch angle. 相似文献
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Jr-Ming MiaoShan-Jen Cheng Sheng-Ju Wu 《International Journal of Hydrogen Energy》2011,36(23):15283-15294
This research presents a systematization and effectiveness approach in promoting the performance of the power density of a Proton Exchange Membrane Fuel Cell (PEMFC) by Metamodel-Based Design Optimization (MBDO). The proposed methodology of MBDO combines the design of experiment (DoE), metamodeling choice and global optimization. The fractional factorial experimental design method can screen important factors and the interaction effects in DoE, and obtain optimal design of the robust performance parameters by Taguchi method. Metamodeling then adopts the ability to establish a non-linear model of a complex PEMFC system configuration of an artificial neural network (ANN) based on the back-propagation network (BPN). Finally, on the many parameters (factors) of optimization, a genetic algorithm (GA) with a high capability for global optimization is used to search the best combination of the parameters to meet the requirement of the quality characteristics. Experimental results confirmed by the test equipment demonstrate that the MBDO approach is effective and systematic in promoting PEMFC performance of power density. 相似文献